MF-SHRED maps point-kinetics trajectories to high-fidelity diffusion fields in one LRA benchmark with ~1-2% errors and ~500x speedup, but two of the three claimed benchmarks are absent.
arXiv preprint arXiv:2111.08481 , year=
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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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Multi-Fidelity Learning with Shallow Recurrent Decoders for Multi-Physics Applications
MF-SHRED maps point-kinetics trajectories to high-fidelity diffusion fields in one LRA benchmark with ~1-2% errors and ~500x speedup, but two of the three claimed benchmarks are absent.
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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.