Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)
Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.
PINNs-TF2, Physics-informed Neural Networks (PINNs) implemented in TensorFlow V2.
Using NVIDIA modulus for airfoil optimizations at different angles.
Physics-Informed Neural Networks Trained with Particle Swarm Optimization
PINNs-JAX, Physics-informed Neural Networks (PINNs) implemented in JAX.
A physics-informed deep learning architecture for inferring bubble dynamics
Utilized PINNs to fit 1D curves and 2D Burgers' Equation
Flood prediction on aerial-views with deep learning. Based on PARFLOOD from HyLab, University of Parma.
Fitting 2D curves or Multi-variable partial Differential Equations
Paper: Modeling water and nitrogen dynamics in unsaturated soils using physics and multi-physics informed neural networks.
A Python General-Purpose Implementation For Physics Informed Neural Networks
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Programmes Python et C++ de mon projet tuteuré de S6. (2024)
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