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- W4296594089 abstract "We propose a novel gray-box modeling algorithm for physical systems governed by stochastic differential equations (SDE). The proposed approach, referred to as the Deep Physics Corrector (DPC), blends approximate physics represented in terms of SDE with deep neural network (DNN). The primary idea here is to exploit DNN to model the missing physics. We hypothesize that combining incomplete physics with data will make the model interpretable and allow better generalization. The primary bottleneck associated with training surrogate models for stochastic simulators is often associated with selecting the suitable loss function. Among the different loss functions available in the literature, we use the conditional maximum mean discrepancy (CMMD) loss function in DPC because of its proven performance. Overall, physics-data fusion and CMMD allow DPC to learn from sparse data. We illustrate the performance of the proposed DPC on four benchmark examples from the literature. The results obtained are highly accurate, indicating its possible application as a surrogate model for stochastic simulators." @default.
- W4296594089 created "2022-09-22" @default.
- W4296594089 creator A5030664714 @default.
- W4296594089 creator A5060792500 @default.
- W4296594089 date "2022-09-20" @default.
- W4296594089 modified "2023-10-18" @default.
- W4296594089 title "Deep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations" @default.
- W4296594089 doi "https://doi.org/10.48550/arxiv.2209.09750" @default.
- W4296594089 hasPublicationYear "2022" @default.
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