Table of Contents
Is differential equations useful in computer science?
Whenever a continuous process is modeled mathematically, chances are high that differential equations are used. Many computer algebra applications to differential equations work indirectly; they help to study and understand properties of the solution space.
Are differential equations useful for machine learning?
Neural differential equations have applications to both deep learning and traditional mathematical modelling. They offer memory efficiency, the ability to handle irregular data, strong priors on model space, high capacity function approximation, and draw on a deep well of theory on both sides.
Are PDEs used in machine learning?
It seems that using machine/deep learning to solve PDEs is very popular (actually, not only in scientific computing, but also in all fields).
What is the most important math for computer science?
Discrete mathematics, linear algebra, number theory, and graph theory are the math courses most relevant to the computer science profession. Different corners of the profession, from machine learning to software engineering, use these types of mathematics.
Is calculus 2 necessary for computer science?
Students who are interested in studying computer science in college have to pass their first calculus class with a grade of C or better to successfully fulfill the calculus 2 prerequisite.
Is abstract algebra useful for computer science?
Theoretical computer scientists make an extensive use of elements from abstract algebra. From the design and analysis of algorithms and cryptographic protocols to the construction of desired combinatorial objects, the use of algebraic structures has repeatedly provided powerful and elegant solutions.
Can neural network solve differential equation?
Now researchers have built new kinds of artificial neural networks that can approximate solutions to partial differential equations orders of magnitude faster than traditional PDE solvers. And once trained, the new neural nets can solve not just a single PDE but an entire family of them without retraining.
Are partial differential equations used in machine learning?
Machine learning method is known to be data driven and lack of robustness and interpretability while numerical partial differential equations have many theoretical foundations for convergence and stability. Several new machine learning based methods have been proposed for solving partial differential equations.