Differentiable hybrid neural modeling for fluid-structure interaction

Fan, Xiantao; Wang, Jianxun

doi:10.48550/arxiv.2303.12971

Cited by 1 publication

(1 citation statement)

References 52 publications

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“…Differentiable programming (DP) serves as a cornerstone in this framework, allowing for the joint optimization of both DNNs and physics-centric components within a unified training environment. The development of differentiable physics solvers and hybrid neural models has recently gained traction, exemplifying their adaptability across various scientific fields [39][40][41][42][43][44][45][46]. Notably, Akhare et al [44] introduced a physics-integrated neural differentiable (PiNDiff) framework, developed for the curing process of composites.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process

Wang,

Akhare,

Chen

et al. 2023

Preprint

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Chemical vapor infiltration (CVI) is a widely adopted manufacturing technique used in producing carbon-carbon and carbon-silicon carbide composites. These materials are especially valued in the aerospace and automotive industries for their robust strength and lightweight characteristics. The densification process during CVI critically influences the final performance, quality, and consistency of these composite materials. Experimentally optimizing the CVI processes is challenging due to long experimental time and large optimization space. To address these challenges, this work takes a modeling-centric approach. Due to the complexities and limited experimental data of the isothermal CVI densification process, we have developed a data-driven predictive model using the physics-integrated neural differentiable (PiNDiff) modeling framework. An uncertainty quantification feature has been embedded within the PiNDiff method, bolstering the model's reliability and robustness. Through comprehensive numerical experiments involving both synthetic and real-world manufacturing data, the proposed method showcases its capability in modeling densification during the CVI process. This research highlights the potential of the PiNDiff framework as an instrumental tool for advancing our understanding, simulation, and optimization of the CVI manufacturing process, particularly when faced with sparse data and an incomplete description of the underlying physics.

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