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New neural networks tackle uncertainty in mechanics simulations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new neural network models, iPANN and fPANN, designed to handle uncertainty in constitutive modeling for mechanics simulations. These models aim to quantify and propagate uncertainty, particularly when dealing with sparse or noisy stress-deformation data. iPANNs establish bounds for free energy density branches, while fPANNs integrate these into a fuzzy-set representation. Both models incorporate physics-based constraints and are trained using a transfer-learning approach, demonstrating their ability to enclose noisy observations and generalize to new data. AI

IMPACT These models offer a physics-consistent method for quantifying and propagating uncertainty in mechanical simulations, potentially improving reliability in engineering applications.

RANK_REASON The cluster contains an academic paper detailing a new research methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New neural networks tackle uncertainty in mechanics simulations

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Somesh Pratap Singh, Govinda Anantha Padmanabha, Jingye Tan, Steven Yang, Reese E. Jones, D. Thomas Seidl, Nikolaos Bouklas ·

    Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling

    arXiv:2607.20339v1 Announce Type: new Abstract: Constitutive modeling under uncertainty remains a central challenge for reliable mechanics simulations, particularly when the available stress-deformation data are sparse, noisy, or heterogeneous. We propose interval and fuzzy physi…

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