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Found 7 papers, 1 papers with code
Real-Time FJ/MAC PDE Solvers via Tensorized, Back-Propagation-Free Optical PINN Training
Despite the ultra-high speed of optical neural networks, training a PINN on an optical chip is hard due to (1) the large size of photonic devices, and (2) the lack of scalable optical memory devices to store the intermediate results of back-propagation (BP).
Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks
Backward propagation (BP) is widely used to compute the gradients in neural network training.
DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design
In this paper, for the first time, we propose DeepOHeat, a physics-aware operator learning framework to predict the temperature field of a family of heat equations with multiple parametric or non-parametric design configurations.
PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks
PIFON-EPT is the first method that can simultaneously reconstruct EP and transmit fields from incomplete noisy MR measurements, providing new opportunities for EPT research.
MR-Based Electrical Property Reconstruction Using Physics-Informed Neural Networks
Electrical properties (EP), namely permittivity and electric conductivity, dictate the interactions between electromagnetic waves and biological tissue.
TT-PINN: A Tensor-Compressed Neural PDE Solver for Edge Computing
Physics-informed neural networks (PINNs) have been increasingly employed due to their capability of modeling complex physics systems.
When and why PINNs fail to train: A neural tangent kernel perspective
In this work, we aim to investigate these questions through the lens of the Neural Tangent Kernel (NTK); a kernel that captures the behavior of fully-connected neural networks in the infinite width limit during training via gradient descent.
