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Very Short Introductions #555
Applied Mathematics: A Very Short Introduction
Mathematics is playing an increasingly important role in society and the sciences, enhancing our ability to use models and handle data. While pure mathematics is mostly interested in abstract structures, applied mathematics sits at the interface between this abstract world and the world in which we live. This area of mathematics takes its nourishment from society and science and, in turn, provides a unified way to understand problems arising in diverse fields.
This Very Short Introduction presents a compact yet comprehensive view of the field of applied mathematics, and explores its relationships with (pure) mathematics, science, and engineering. Explaining the nature of applied mathematics, Alain Goriely discusses its early achievements in physics and engineering, and its development as a separate field after World War II. Using historical examples, current applications, and challenges, Goriely illustrates the particular role that mathematics plays in the modern sciences today and its far-reaching potential.
ABOUT THE The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.
This Very Short Introduction presents a compact yet comprehensive view of the field of applied mathematics, and explores its relationships with (pure) mathematics, science, and engineering. Explaining the nature of applied mathematics, Alain Goriely discusses its early achievements in physics and engineering, and its development as a separate field after World War II. Using historical examples, current applications, and challenges, Goriely illustrates the particular role that mathematics plays in the modern sciences today and its far-reaching potential.
ABOUT THE The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.
176 pages, Paperback
Published April 22, 2018
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March 14, 2023
هذا الكتاب كان دافع جعلني أزيد من احترامي للرياضيات التطبيقية كفرع مستقل ووطيد في الرياضيات، وغير نظرتي عنها من كونها فيزياء في ثوب رياضياتي.
February 24, 2018
This little book in Oxford University Press's vast, ever-expanding 'A Very Short Introduction' series starts off with a very positive note. After a quote from Groucho Marx, Alain Goriely takes us on a jovial tour of what 'applied mathematics' means. I was slightly surprised it needed such an introduction. It seems fairly obvious that it's mathematics that is, erm, applied, rather than maths for maths' sake. However, in the process Goriely gives us some of the basics involved.
One thing I would have liked to have seen, but didn't get, was more of an exploration of the boundary between applied maths and theoretical physics. (Cambridge even has a 'Department of Applied Mathematics and Theoretical Physics'.) I appreciate that some applied mathematics is used in other disciplines, but it does seem that the bulk of it is in physics, and the distinction between what an applied mathematician and a theoretical physicist does seems fairly fuzzy, to say the least.
After the introduction, Goriely starts with simple applications, such as working out the cooking time for a turkey, through more and more complex uses, gradually adding in more powerful mathematics. Although you don't need to know how to use the heavier duty tools, you will meet differential equations and even partial differential equations along the way. The trouble with familiar applications, of course, is that it's easy to get lost in the reality of it, which left me worrying for Goriely's health. He reckons a 5 kg turkey cooks in 2.5 hours, where Delia Smith (who surely knows better) would give it at least 4 hours. I'm with Delia on this.
There's some really good material here on the use of dimensions and scaling, but already the way the information is presented is becoming quite difficult to absorb. Not surprisingly there are equations - but they are used far too liberally, while technical terms are introduced often without explanation, or with explanations that don't really work. We move on to mathematical modelling and solving equations. Once again, simply following the argument is difficult without already having a reasonable grasp of at least A-level maths.
There are all sorts of good things covered in the book, from knot theory (and its relevance to DNA) to JPEG compression. It's just a shame that, either because the book is so short, or because the author expects too much of the reader, the information in it is not presented in a way that is particularly accessible.
One thing I would have liked to have seen, but didn't get, was more of an exploration of the boundary between applied maths and theoretical physics. (Cambridge even has a 'Department of Applied Mathematics and Theoretical Physics'.) I appreciate that some applied mathematics is used in other disciplines, but it does seem that the bulk of it is in physics, and the distinction between what an applied mathematician and a theoretical physicist does seems fairly fuzzy, to say the least.
After the introduction, Goriely starts with simple applications, such as working out the cooking time for a turkey, through more and more complex uses, gradually adding in more powerful mathematics. Although you don't need to know how to use the heavier duty tools, you will meet differential equations and even partial differential equations along the way. The trouble with familiar applications, of course, is that it's easy to get lost in the reality of it, which left me worrying for Goriely's health. He reckons a 5 kg turkey cooks in 2.5 hours, where Delia Smith (who surely knows better) would give it at least 4 hours. I'm with Delia on this.
There's some really good material here on the use of dimensions and scaling, but already the way the information is presented is becoming quite difficult to absorb. Not surprisingly there are equations - but they are used far too liberally, while technical terms are introduced often without explanation, or with explanations that don't really work. We move on to mathematical modelling and solving equations. Once again, simply following the argument is difficult without already having a reasonable grasp of at least A-level maths.
There are all sorts of good things covered in the book, from knot theory (and its relevance to DNA) to JPEG compression. It's just a shame that, either because the book is so short, or because the author expects too much of the reader, the information in it is not presented in a way that is particularly accessible.
July 1, 2018
"'The miracle of the appropriateness of the language of mathematics for the formulation of the laws of physics is a wonderful gift which we neither understand not deserve.'" - Eugene Wigner, 1960
This was an interesting addition to the Very Short Introduction series, balanced between fully accessible content and morsels of new applications of mathematical theory beginning to influence science today. An excellent book in its moments, bringing personality to equations, which is an achievement in itself, but vague about its targeted readership.
I was motivated to buy this book for my Baby Adam after attending a talk by the author, Professor Alain Goriely, introducing applied mathematics to a general audience at the University of Oxford. During this talk, Goriely absolutely sparkled, combining absurd humour with unpublished teasers about fascinating new mathematical modelling of the brain. Goriely sparkled in person, and I was keen to listen back again with Baby Adam and Little just a few hours later.
For me, applied maths has a fairly intuitive definition. Apparently not. Early applied mathematician Richard Courant once said:"Applied mathematics is not a definable scientific field but a human attitude." To provide historical context, Goriely emphasises the novelty of the divide between maths and the scientists. Less than 300 years ago these disciplines were not practiced by specialists, all were the domain of 'natural scientists' like Newton, Descartes, and Leibnitz studied everything from medicine to abstract maths. In the 19th century, the sheer weight of knowledge necessitated a split in the sciences. I'm not sure exactly when and where the line was drawn between applied maths and the pure unearthly stuff studied by my husband ("Mathematics stands apart from scientific disciplines because it is not restricted by reality. It proceeds only through logic and is only restricted by out imagination."), but there is an intuitive divide between maths creating for its own sake and that applied to specific problems with reallife parallels.
The first couple of chapters are a lengthy examination of how dimensional analysis, or trying to balance and compare physical equations can provide information about what variables are involved in a physical process. It's fairly wordy. I would have preferred a more spacious visual explanation, or possibly an appendix for all the dimensional analysis stuff. It was heavy going and relied on very old examples, like the length of time it takes a candle to burn or the time a roast takes in the oven. Most of the examples are looking for linear relationships based on comparing data from different examples, an approach the completely avoids causation. "It is fascinating to compare the metabolism of a mouse to a human, but it will not shed much light on how a human's metabolism works other than that it is different from a mouse's." I would be concerned that some readers would be put of by these chapters and miss out on the good stuff to come after.
"Applied mathematics is best characterised by three intertwined areas: modelling, theory, and methods."
For me, the book really picked up when it focused on the methods used by applied mathematicians, I'm a neuroscientist, and it's an exciting time for maths and neuroscience as more and more modelling is becoming an accepted tool to better understand the brain. "Out of this mutual state of respect and ignorance slowly emerges a fragment of a problem, a question, an idea, something worth trying either with a simple model or an experiment." It;s a really interesting approach. Just when neuroscience is adding layers and layers of complexity to brain function, with new regulators for genes, proteins, neurotransmitters, action potentials, being discovered on a regular basis with the potential to influence many processes at once. Neuroscience has in some sense become a 5,000 piece jigsaw puzzle, with each research group trying to work out what the shape of their particular piece is before even thinking about the overall picture. "Models are the ultimate form 0f quantification as since all variables and parameters must be properly defined and quantified for the equations to make sense." Mathematical modelling is a great complement to this approach because at its heart, modelling is looking for the simplest model which explains most of the data, stripping away minor variables to focus on the process as a whole.
Goriely uses the example of plotting the trajectory of the ball through the air as a process that can be readily modelled. He points out that Newton's laws of physics can be used to model the throw, giving some information about the way the ball will move in the complex real life environment. But he also acknowledges the intelligence and insight needed to design useful models. "Modelling is not an exercise in abstract physics. It must rely on sound judgement and on the understanding of the underlying problem." He highlights the absurd possibility of including general relativity in the model of the ball's path to highlight the importance of selecting the most relevant variables, keeping the model within the "Goldilocks zone" where it is complex enough to mimic real life without growing so complicated that mathematicians and scientists can gain little insight from it. (Are you taking notes, Moltan Zolnar?) When talking about the brain, Goriely suggests, "the system may be too complex or not yet sufficiently understood to merit a proper mathematical formulation." That's an interesting restraint. But is this the product of modern scientists insisting that new discoveries in neuroscience are so crucial we cannot understand the brain without them, or a reflection of the reality in which many of the basic mechanical and chemical processes of thought are at least partially understood?
Next Goriely addresses the role of equations in applied mathematics. In many ways, solving equations is a reminder of slogging through all those simultaneous equations at school. But Goriely really lifts this whole topic up and makes it interesting, even giving the equations personality. He begins with the simple quadratics, and as early as the 1920s Abel and Galois had already shown that there is no general formula for qunitic and higher equations. I was fascinated to read about Sophia Kovalevskia, the first female tenured professor of mathematics in Europe, who added to this by showing that several equations of motion do not have explicity solutions. This all came to a head in 1889, when Poincare realised he has made a mistake in his equations for three celestial bodies orbiting one another. He had assumed that erratic unpredictable behaviour was not possible. "The complexity of this figure is striking, and I shall not even try to draw it." - Poincare But it was, and the whole question behind solving equations was changed to "what are the possible shapes in phase-space for the solutions of differential equations?" Suddenly equations have life, they are returned to their geometric grandeur.
The final chapters are a tease and a treat. Goriely offers some instances in which applied mathematics have advanced the sciences. These include William Hamilton's "quaternions" which extend the idea of complex numbers to include additional terms and have now been used to decrease the number of characters needed in computer programming. I was not aware of the bizarre abstract object, the soliton, non-linear wave which is made up of energy but behaves like a particle. These waves are not altered in shape when they collide and can exist even in the most 'wavey' of waves, the water wave. Another very cool example was the related 'wavelet method' for data compression adapted from Fourier's wave transforms to compress image files by preserving boundaries of high contrast areas for a much greater high-resolution compression that averaging sets of adjacent pixels. Hounsfield and Cormack received the 1963 Nobel Prize for applying this method to compression of CT scan images. During his acceptance speech, Hounsfield admitted he had only recently discovered that the method had originally been published in 1917 by Johan Radon. Image compression remains an important field. Fields Medalist Terry Tao among others are working on "compressive sampling" methods which could revolutionise image acquisition as well as storage. "the way digital camera work, they acquire the whole data set, then compress the signal and store a fraction of it." It's a very exciting time for maths and computer science.
But back to the brain. "Whereas much is known about the brain at a functional level, comparatively little is known and understood at the geometric, physical, and mechanical levels." It was interesting to read about the origin of the now ubiquitous mathematics behind neural networks which are being used to analyse interactions between anything from genes and proteins to whole regions of the human brain. These networks were first studied in depth by Stephen Strogatz, who in 1998 proposed a "small worldness" effect in networks which have both "short average path length" (things on the opposite side of the network are connected) and "high clustering", giving rise to networks with high connectivity. I would love to read more about the strengths and limitations of this approach but then again, "Physical studies of the brain are difficult." I can testify to that.
A final gem was reading about the origins and applications of knot theory, a field of geometry with wonderfully satisfying visual diagrams which characterise knots based on the number of self-crosses. Shortly after I met my partner, he was studying their tangled properties. Knot theory began as a mathematical discipline in the 18th century when Lord Kelvin convinced himself that atoms were tangled clumps of energy, and asked asked his mathematical friends Taite and Little to come up with a formal categorisation of knots. Although Kelvin abandoned his theory, knot theory is alive and well in biochemistry with the discovery of DNA de-tangling enzymes which James C. Wang christened "topoisomerases", and whose mathematical operations were only hinted at in my undergraduate syllabus.
An interesting book which varied in its ability to grip me, perhaps aimed at those who are comfortable with A-level maths concepts rather than the general readership I was expecting. All the same, I learned a lot!
This was an interesting addition to the Very Short Introduction series, balanced between fully accessible content and morsels of new applications of mathematical theory beginning to influence science today. An excellent book in its moments, bringing personality to equations, which is an achievement in itself, but vague about its targeted readership.
I was motivated to buy this book for my Baby Adam after attending a talk by the author, Professor Alain Goriely, introducing applied mathematics to a general audience at the University of Oxford. During this talk, Goriely absolutely sparkled, combining absurd humour with unpublished teasers about fascinating new mathematical modelling of the brain. Goriely sparkled in person, and I was keen to listen back again with Baby Adam and Little just a few hours later.
For me, applied maths has a fairly intuitive definition. Apparently not. Early applied mathematician Richard Courant once said:"Applied mathematics is not a definable scientific field but a human attitude." To provide historical context, Goriely emphasises the novelty of the divide between maths and the scientists. Less than 300 years ago these disciplines were not practiced by specialists, all were the domain of 'natural scientists' like Newton, Descartes, and Leibnitz studied everything from medicine to abstract maths. In the 19th century, the sheer weight of knowledge necessitated a split in the sciences. I'm not sure exactly when and where the line was drawn between applied maths and the pure unearthly stuff studied by my husband ("Mathematics stands apart from scientific disciplines because it is not restricted by reality. It proceeds only through logic and is only restricted by out imagination."), but there is an intuitive divide between maths creating for its own sake and that applied to specific problems with reallife parallels.
The first couple of chapters are a lengthy examination of how dimensional analysis, or trying to balance and compare physical equations can provide information about what variables are involved in a physical process. It's fairly wordy. I would have preferred a more spacious visual explanation, or possibly an appendix for all the dimensional analysis stuff. It was heavy going and relied on very old examples, like the length of time it takes a candle to burn or the time a roast takes in the oven. Most of the examples are looking for linear relationships based on comparing data from different examples, an approach the completely avoids causation. "It is fascinating to compare the metabolism of a mouse to a human, but it will not shed much light on how a human's metabolism works other than that it is different from a mouse's." I would be concerned that some readers would be put of by these chapters and miss out on the good stuff to come after.
"Applied mathematics is best characterised by three intertwined areas: modelling, theory, and methods."
For me, the book really picked up when it focused on the methods used by applied mathematicians, I'm a neuroscientist, and it's an exciting time for maths and neuroscience as more and more modelling is becoming an accepted tool to better understand the brain. "Out of this mutual state of respect and ignorance slowly emerges a fragment of a problem, a question, an idea, something worth trying either with a simple model or an experiment." It;s a really interesting approach. Just when neuroscience is adding layers and layers of complexity to brain function, with new regulators for genes, proteins, neurotransmitters, action potentials, being discovered on a regular basis with the potential to influence many processes at once. Neuroscience has in some sense become a 5,000 piece jigsaw puzzle, with each research group trying to work out what the shape of their particular piece is before even thinking about the overall picture. "Models are the ultimate form 0f quantification as since all variables and parameters must be properly defined and quantified for the equations to make sense." Mathematical modelling is a great complement to this approach because at its heart, modelling is looking for the simplest model which explains most of the data, stripping away minor variables to focus on the process as a whole.
Goriely uses the example of plotting the trajectory of the ball through the air as a process that can be readily modelled. He points out that Newton's laws of physics can be used to model the throw, giving some information about the way the ball will move in the complex real life environment. But he also acknowledges the intelligence and insight needed to design useful models. "Modelling is not an exercise in abstract physics. It must rely on sound judgement and on the understanding of the underlying problem." He highlights the absurd possibility of including general relativity in the model of the ball's path to highlight the importance of selecting the most relevant variables, keeping the model within the "Goldilocks zone" where it is complex enough to mimic real life without growing so complicated that mathematicians and scientists can gain little insight from it. (Are you taking notes, Moltan Zolnar?) When talking about the brain, Goriely suggests, "the system may be too complex or not yet sufficiently understood to merit a proper mathematical formulation." That's an interesting restraint. But is this the product of modern scientists insisting that new discoveries in neuroscience are so crucial we cannot understand the brain without them, or a reflection of the reality in which many of the basic mechanical and chemical processes of thought are at least partially understood?
Next Goriely addresses the role of equations in applied mathematics. In many ways, solving equations is a reminder of slogging through all those simultaneous equations at school. But Goriely really lifts this whole topic up and makes it interesting, even giving the equations personality. He begins with the simple quadratics, and as early as the 1920s Abel and Galois had already shown that there is no general formula for qunitic and higher equations. I was fascinated to read about Sophia Kovalevskia, the first female tenured professor of mathematics in Europe, who added to this by showing that several equations of motion do not have explicity solutions. This all came to a head in 1889, when Poincare realised he has made a mistake in his equations for three celestial bodies orbiting one another. He had assumed that erratic unpredictable behaviour was not possible. "The complexity of this figure is striking, and I shall not even try to draw it." - Poincare But it was, and the whole question behind solving equations was changed to "what are the possible shapes in phase-space for the solutions of differential equations?" Suddenly equations have life, they are returned to their geometric grandeur.
The final chapters are a tease and a treat. Goriely offers some instances in which applied mathematics have advanced the sciences. These include William Hamilton's "quaternions" which extend the idea of complex numbers to include additional terms and have now been used to decrease the number of characters needed in computer programming. I was not aware of the bizarre abstract object, the soliton, non-linear wave which is made up of energy but behaves like a particle. These waves are not altered in shape when they collide and can exist even in the most 'wavey' of waves, the water wave. Another very cool example was the related 'wavelet method' for data compression adapted from Fourier's wave transforms to compress image files by preserving boundaries of high contrast areas for a much greater high-resolution compression that averaging sets of adjacent pixels. Hounsfield and Cormack received the 1963 Nobel Prize for applying this method to compression of CT scan images. During his acceptance speech, Hounsfield admitted he had only recently discovered that the method had originally been published in 1917 by Johan Radon. Image compression remains an important field. Fields Medalist Terry Tao among others are working on "compressive sampling" methods which could revolutionise image acquisition as well as storage. "the way digital camera work, they acquire the whole data set, then compress the signal and store a fraction of it." It's a very exciting time for maths and computer science.
But back to the brain. "Whereas much is known about the brain at a functional level, comparatively little is known and understood at the geometric, physical, and mechanical levels." It was interesting to read about the origin of the now ubiquitous mathematics behind neural networks which are being used to analyse interactions between anything from genes and proteins to whole regions of the human brain. These networks were first studied in depth by Stephen Strogatz, who in 1998 proposed a "small worldness" effect in networks which have both "short average path length" (things on the opposite side of the network are connected) and "high clustering", giving rise to networks with high connectivity. I would love to read more about the strengths and limitations of this approach but then again, "Physical studies of the brain are difficult." I can testify to that.
A final gem was reading about the origins and applications of knot theory, a field of geometry with wonderfully satisfying visual diagrams which characterise knots based on the number of self-crosses. Shortly after I met my partner, he was studying their tangled properties. Knot theory began as a mathematical discipline in the 18th century when Lord Kelvin convinced himself that atoms were tangled clumps of energy, and asked asked his mathematical friends Taite and Little to come up with a formal categorisation of knots. Although Kelvin abandoned his theory, knot theory is alive and well in biochemistry with the discovery of DNA de-tangling enzymes which James C. Wang christened "topoisomerases", and whose mathematical operations were only hinted at in my undergraduate syllabus.
An interesting book which varied in its ability to grip me, perhaps aimed at those who are comfortable with A-level maths concepts rather than the general readership I was expecting. All the same, I learned a lot!
January 12, 2023
Applied Mathematics : A Very Short Introduction (2018) by Alain Goriely is an excellent very short introduction covering Applied Mathematics. Goriely is an applied mathematician at Oxford.
The chapters start with Marx Brothers quotes and are fun to read. First a description of where applied mathematics is sitting is provided. Then a model is provided for cooking birds. Then models are further described with more complicated models. Goriely then delves into differential equations, then partial differential equations and further into how various phenomena are described with applied math. The unreasonable effectiveness of mathematics is then expounded on with examples of mathematical curiosities from the 1800s and their applications today. Finally a discussion of where applied math is going is given.
Applied Mathematics is a really good short introduction, it’s readable, informative and nicely short. There are some equations in the book but not too many. It would be a great book for a teenager curious to find out about applied mathematics. Goriely describes the difficulty in describing what he does at parties, now he can point them to this excellent book.
The chapters start with Marx Brothers quotes and are fun to read. First a description of where applied mathematics is sitting is provided. Then a model is provided for cooking birds. Then models are further described with more complicated models. Goriely then delves into differential equations, then partial differential equations and further into how various phenomena are described with applied math. The unreasonable effectiveness of mathematics is then expounded on with examples of mathematical curiosities from the 1800s and their applications today. Finally a discussion of where applied math is going is given.
Applied Mathematics is a really good short introduction, it’s readable, informative and nicely short. There are some equations in the book but not too many. It would be a great book for a teenager curious to find out about applied mathematics. Goriely describes the difficulty in describing what he does at parties, now he can point them to this excellent book.
December 6, 2021
Stop watching those ads-ridden YouTube math videos and buy this book. At $7.50 for the Kindle edition, it’s cheaper than a banana and a bottled water at LAX. Witty, illuminating, judicious illustrate—what more could you ask for?
August 30, 2019
The author is apparently very knowledgeable about mathematics and a number of related fields. He not only discusses applied mathematics, but how it relates to these other fields, which is welcome. To his credit, he also employs quotes throughout to inject some levity into what might otherwise be a dry topic. Unfortunately, the book is not digestible by newcomers to the field, which likely includes a sizeable portion of prospective readers. Goriely notes on page 102 of the print edition that "mathematics … has its own language which can exclude those who do not speak it." He apparently thought that readers would be mathematicians, as complicated equations appear frequently throughout the book. And his prose does little to mitigate the challenging mathematical ideas. In a bit of irony, the author points to the "wonderful" Very Short Introduction to Mathematics by Timothy Gowers which I found outstanding and very approachable as well.
February 27, 2022
An intro to applied maths that takes you from crude algebraic models to differential equations, chaotic systems of differential equations, partial differential equations, networks, knots, etc.
I recommend it to people that know some college level mathematics that are aware of the topics at hand, otherwise this book might be a bit too much.
Many of the books in the "A very Short Introduction" catalogue get moderate reviews and I get it, many of the books are hard to get into and they really are short, but under the constraints this series works on, Alain's book is one of my favorites.
I recommend it to people that know some college level mathematics that are aware of the topics at hand, otherwise this book might be a bit too much.
Many of the books in the "A very Short Introduction" catalogue get moderate reviews and I get it, many of the books are hard to get into and they really are short, but under the constraints this series works on, Alain's book is one of my favorites.
January 16, 2020
A great survey of the nature and vitality of applied mathematics. As as someone with a degree in applied math, I found it interesting and insightful. I even learned something new (quaternions!). I loved the references to history and applications in the present day, bringing everything together. The recommended readings at the end was an excellent coda. Seeing one of the textbooks I used in undergrad listed gave me a sense of pride in my education.
December 26, 2022
I found this book to be a helpful introduction to applied mathematics and enjoyed reading it. The way it demonstrates the power of simple equations to provide important insights was particularly impressive. One aspect I think the author could have covered more in-depth is the process of converting mathematical models into computer simulations.
May 17, 2025
Very very interesting to read about all the real world applications about a bunch of the math I’ve learned thus far in college. Loved the connection to Strogatz’s works too. I wish there was more of a focus on matrix algebra and number theory with regard to cryptography. It was repeatedly mentioned, but never explored. Overall cool book, thanks jae!
March 30, 2019
Whew!!
Very difficult for this reader who hasn't been in a math class for close to 60 years. I may have rated it higher if I understood it more
Very difficult for this reader who hasn't been in a math class for close to 60 years. I may have rated it higher if I understood it more
December 16, 2019
Very nice sampler of applied mathematics. Also manages to make a case for the importance of pure mathematics. A great book.
June 25, 2020
I liked this a lot.
October 4, 2020
Неплохо, набор примеров использования методов прикладной математики. Ничего особенного, но шутки вначале глав отличные.
December 29, 2020
4.5/5
August 23, 2021
If I could travel back in time and meet my high school self, I would give him this book.
October 3, 2022
A good quick intro into a lot of different areas of applied math. The end of the book has a list of references for further learning. I'll probably be using this book as a simple reference.
February 6, 2023
An interesting overview
April 19, 2024
Fun, interesting, and yes, short.
July 1, 2024
Very informative, very helpful.
Displaying 1 - 20 of 20 reviews