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Found 3 papers, 1 papers with code
Theory-Guided Machine Learning for Process Simulation of Advanced Composites
A recent successful implementation of a TGML model to assess producibility of aerospace composite parts is presented.
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
We present a Physics-Informed Neural Network (PINN) to simulate the thermochemical evolution of a composite material on a tool undergoing cure in an autoclave.
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness
Our surrogates target stochastic simulators where the number of random variables itself can be stochastic and potentially unbounded.
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