Presents DHPO and a pretrained DeepONet inverse modeling framework that discovers unknown PDE terms and infers parameters across equation families with O(10^-2) solution and O(10^-3) parameter errors on benchmarks.
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Active learning with physics-informed surrogates achieves comparable accuracy for a glycol heat exchanger digital twin using only one-fifth the high-fidelity simulation trajectories needed by random sampling.
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Learning Hidden Physics and System Parameters with Deep Operator Networks
Presents DHPO and a pretrained DeepONet inverse modeling framework that discovers unknown PDE terms and infers parameters across equation families with O(10^-2) solution and O(10^-3) parameter errors on benchmarks.
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Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning
Active learning with physics-informed surrogates achieves comparable accuracy for a glycol heat exchanger digital twin using only one-fifth the high-fidelity simulation trajectories needed by random sampling.