Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
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Updated
Nov 18, 2024 - Julia
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Public repository for the proposal “Physics-Informed Machine Learning Simulator for Wildfire Propagation” - MLJC University of Turin - ProjectX2020 Competition (UofT AI)
Code for "Learning Local Control Barrier Functions for Safety Control of Hybrid Systems"
Comparison of numerical solutions of the 1-D time-independent Schrödinger equation obtained through FDM, FEM and the neural network approach.
Numerical solution and uncertainty quantification of Pennes' bioheat transfer equation in 1-D using deep neural network solver.
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