2021年2月9日 · Here, we propose a new deep learning method -- physics-informed neural networks with hard constraints (hPINNs) -- for solving topology optimization. hPINN leverages the recent development of PINNs for solving PDEs, and …

Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).

It is highly recommended to utilize implementations of Physics-Informed Neural Networks (PINNs) available in PyTorch, JAX, and TensorFlow v2.

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2023年6月15日 · To address the issue of low accuracy and convergence problems of existing PINNs, we propose a self-training physics-informed neural network, ST-PINN. Specifically, ST-PINN introduces a pseudo label based self-learning algorithm during training.

Steven Pinn is a General Manager at MyEyeDr based in Vienna, Virginia. Previously, Steven was a Store Market Manager at LensCrafters and also held positions at Bloomingdale's, Hugo Boss, Nordstrom, Club Monaco.

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To address the problem of low accuracy and convergence problems of existing PINNs, we propose a selftraining physics-informed neural network, ST-PINN. Specifically, ST-PINN introduces a pseudo label based self-learning algorithm during training.