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 9, 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" by S. Yang, Y. Chen, X. Yin, R. Mangharam
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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