Výsledky vyhledávání - "Physics-constrained learning"

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Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models

Autor: Tan, Kaiyuan, Li, Peilun, Beckers, Thomas

Modeling the dynamics of flexible objects has become an emerging topic in the community as these objects become more present in many applications, e.g., soft robotics. Due to the properties of flexible materials, the movements of soft objects are oft

Externí odkaz: http://arxiv.org/abs/2406.11809

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A Non-intrusive Approach for Physics-constrained Learning with Application to Fuel Cell Modeling

Autor: Srivastava, Vishal, Sulzer, Valentin, Mohtat, Peyman, Siegel, Jason B., Duraisamy, Karthik

A data-driven model augmentation framework, referred to as Weakly-coupled Integrated Inference and Machine Learning (IIML), is presented to improve the predictive accuracy of physical models. In contrast to parameter calibration, this work seeks corr

Externí odkaz: http://arxiv.org/abs/2207.10819

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Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning

Autor: Xu, Kailai, Tartakovsky, Alexandre M., Burghardt, Jeff, Darve, Eric

We propose a novel approach to model viscoelasticity materials using neural networks, which capture rate-dependent and nonlinear constitutive relations. However, inputs and outputs of the neural networks are not directly observable, and therefore com

Externí odkaz: http://arxiv.org/abs/2005.04384

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Akademický článek

A non-intrusive approach for physics-constrained learning with application to fuel cell modeling.

Autor: Srivastava, Vishal¹ (AUTHOR) vsriv@umich.edu, Sulzer, Valentin² (AUTHOR), Mohtat, Peyman² (AUTHOR), Siegel, Jason B.² (AUTHOR), Duraisamy, Karthik¹ (AUTHOR)

Publikováno v: Computational Mechanics. Aug2023, Vol. 72 Issue 2, p411-430. 20p.

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Physics Constrained Learning for Data-driven Inverse Modeling from Sparse Observations

Autor: Xu, Kailai, Darve, Eric

Deep neural networks (DNN) have been used to model nonlinear relations between physical quantities. Those DNNs are embedded in physical systems described by partial differential equations (PDE) and trained by minimizing a loss function that measures

Externí odkaz: http://arxiv.org/abs/2002.10521

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