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Article
Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading
Additive manufacturing has been recognized as an industrial technological revolution for manufacturing, which allows fabrication of materials with complex three-dimensional (3D) structures directly from comput...
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Article
Open AccessPhysics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines
This paper presents a physics-informed neural network (PINN) approach for monitoring the health of diesel engines. The aim is to evaluate the engine dynamics, identify unknown parameters in a “mean value” mode...
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Article
Open AccessVariational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems
Physics-informed deep learning has recently emerged as an effective tool for leveraging both observational data and available physical laws. Physics-informed neural networks (PINNs) and deep operator networks ...
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Article
Open AccessPhysics-informed neural networks with residual/gradient-based adaptive sampling methods for solving partial differential equations with sharp solutions
We consider solving the forward and inverse partial differential equations (PDEs) which have sharp solutions with physics-informed neural networks (PINNs) in this work. In particular, to better capture the sha...
