Colección de trabajos asociados al ramo con el nombre del repositorio dictado por la Escuela de Ingeniería de la Universidad de Chile.
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Updated
Jun 1, 2024 - Jupyter Notebook
Colección de trabajos asociados al ramo con el nombre del repositorio dictado por la Escuela de Ingeniería de la Universidad de Chile.
Paper: Modeling water and nitrogen dynamics in unsaturated soils using physics and multi-physics informed neural networks.
Modeling of mechanical wave propagation using FEM and PINNs.
Comparative analysis of computational modeling of acoustic wave propagation using neural network-based methods.
A pytorch framework for solving PDEs via Physics Informed Neural Networks (PINNs)!
Awesome-spatial-temporal-scientific-machine-learning-data-mining-packages. Julia and Python resources on spatial and temporal data mining. Mathematical epidemiology as an application. Most about package information. Data Sources Links and Epidemic Repos are also included.
A repository to learn about Physics-informed Neural Networks!
Programmes Python et C++ de mon projet tuteuré de S6. (2024)
This is a repository for CS4ML. It is a general framework for active learning in regression problems. It approximates a target function arising from general types of data, rather than pointwise samples.
PINNs/FDM for 1D/2D heat and burgers equations
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
A Python General-Purpose Implementation For Physics Informed Neural Networks
Fitting 2D curves or Multi-variable partial Differential Equations
Solve 2D Navier-Stokes Equation using Physics-Informed Network
Repository for the Deep Learning in Scientific Computing course offered in Spring 2022 at ETH Zürich: Deep learning project with the aim of studying the preliminary design of a thermal energy storage.
PINNs-JAX, Physics-informed Neural Networks (PINNs) implemented in JAX.
Machine learning for state-to-state
C++ automatic differentiation library with no dependencies and arbitrary higher order derivatives, stand-alone, header only
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