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RISPhysics-Informed Fully Convolutional Networks for Forward Prediction of Temperature Field and Inverse Estimation of Thermal Diffusivity
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Physics-informed neural networks (PINNs) are a novel approach to solving partial differential equations (PDEs) through deep learning. They offer a unified manner for solving forward and inverse problems, which is beneficial ...
Physics-Based Compressive Sensing to Enable Digital Twins of Additive Manufacturing Processes
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Sensors play an important role in monitoring manufacturing processes and update their digital twins. However, the data transmission bandwidth and sensor placement limitations in the physical systems may not allow us to ...
Physics-Constrained Bayesian Neural Network for Bias and Variance Reduction
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: When neural networks are applied to solve complex engineering problems, the lack of training data can make the predictions of the surrogate inaccurate. Recently, physics-constrained neural networks were introduced to ...
Finite-Volume Physics-Informed U-Net for Flow Field Reconstruction With Sparse Data
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Fluid dynamics is governed by partial differential equations (PDEs) which are solved numerically. The limitations of traditional methods in data assimilation hinder their effective engagement with experiments. Physics-informed ...
PhysicsConstrained Bayesian Neural Network for Bias and Variance Reduction
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: When neural networks are applied to solve complex engineering problems, the lack of training data can make the predictions of the surrogate inaccurate. Recently, physicsconstrained neural networks were introduced to integrate ...