Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
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Updated
Jul 2, 2024 - Python
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.
Learning function operators with neural networks.
Supporting code for "reduced order modeling using advection-aware autoencoders"
Π-ML: Learn data-driven similarity theories of physical problems
Deep learning library for solving differential equations and more
Includes codes for the forthcoming paper, "Learning to generate synthetic human mobility data: A physics-regularized Gaussian process approach based on multiple kernel learning"
MeshfreeFlowNet: Physical Constrained Space Time Super-Resolution
Deep learning for Engineers - Physics Informed Deep Learning
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
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