Scientific Learning project on the monodomain equation
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Updated
Jun 19, 2024 - Python
Scientific Learning project on the monodomain equation
Develop and implement a PINN to solve the Burguers' Equation. This is the first step in the process of learning how to implement this PINN.
Improving hyperthermia treatment by controlling temperature distribution in both 1D and 2D domains and thermal energy applied to cutaneous and subcutaneous tissues, through Bio-Heat equation with Physics-Informed Neural Networks (PINNs).
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
Using cumulative infected cases data from Italy, South Korea, and USA, we simulate mitigation measures including; early detection of infectives, social distancing and contact tracing
Physics Informed Neural Networks
A library for scientific machine learning and physics-informed learning
Universal Physics Informed NN (UPINN) applied on Burger's Equation
Physics-Informed Neural Networks (PINNs) with PyTorch
Due to the public health intervention and public response, transmission of covid is a nonlinear time-dependent function. using cumulative infected cases data from Italy and USA, we learn time varying transmission rates.
A PyTorch based deep-learning toolkit for developing DL models for physical systems
Solving partial differential equations with PINNS (Direct and Inverse Problem)
Physics Informed Neural Networks
C++ automatic differentiation library with no dependencies and arbitrary higher order derivatives, stand-alone, header only
PINN example for 2D square cavity flow in multiple conditions.
Implementation of a PINN solver for biological differential equations
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