chr1shr / am205_examples
Harvard Applied Math 205: Code Examples
☆82Updated 2 years ago
Alternatives and similar repositories for am205_examples:
Users that are interested in am205_examples are comparing it to the libraries listed below
- Harvard AM205: group activity files☆15Updated 3 years ago
- 18.336 - Fast Methods for Partial Differential and Integral Equations☆184Updated 9 months ago
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆25Updated 2 years ago
- ME 697 - Advanced Scientific Machine Learning☆20Updated last week
- Harvard Applied Math 225: Code Examples☆24Updated 2 years ago
- 18.303 - Linear PDEs course☆140Updated last year
- ☆41Updated 4 years ago
- 18.335 - Introduction to Numerical Methods course☆512Updated this week
- Code for the paper: Solving and Learning Nonlinear PDEs with Gaussian Processes☆36Updated 3 months ago
- APPM 5630 at CU Boulder☆45Updated last week
- A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software☆48Updated last year
- Course notes for graduate-level class on numerical methods for deep learning☆50Updated 3 years ago
- Julia code for the book Numerical Linear Algebra☆117Updated 2 years ago
- Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems☆102Updated 3 weeks ago
- Python codes for Introduction to Computational Stochastic PDE☆40Updated last week
- ☆35Updated 3 years ago
- SINDy (Sparse Identification of Nonlinear Dynamics) algorithms☆72Updated 2 years ago
- Matlab codes accompanying Numerical Methods for Stochastic Partial Differential Equations with White Noise☆26Updated 7 years ago
- 18.S096 - Applications of Scientific Machine Learning☆309Updated 2 years ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆54Updated 2 years ago
- ☆95Updated 3 weeks ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆67Updated 4 years ago
- This is a course material for numerical optimization to be taught in summer 2020☆39Updated 4 years ago
- Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)☆55Updated 4 years ago
- Optimal Transport for Dummies - Code, slides and article☆32Updated 7 years ago
- A Python implementation of Chebfun☆135Updated last year
- ☆27Updated 6 years ago
- ☆112Updated 7 years ago
- Numerical differentiation with regularization, allowing differentiation of noisy data without amplifying noise. Uses total variation and …☆29Updated 6 years ago
- Deep Learning application to the partial differential equations☆30Updated 6 years ago