arXiv AI July 16, 2026 By Giansalvo Cirrincione, Filippo Grassia

When is the combined load identifiable from a stress-intensity profile? A coupled forward-inverse study on SIFBench finite-element data

arXiv:2607. 13074v1 Announce Type: cross Abstract: This work studies the inverse problem of recovering the relative magnitudes of the tension, bending, and bearing loads acting on a crack from its stress-intensity-factor profile along the crack front, using the public SIFBench finite-element data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 7

Discrete energy as an exact label-free training objective for finite-element surrogates

arXiv:2608. 05437v1 Announce Type: cross Abstract: Supervised training of finite-element (FE) surrogate models requires reference solutions, and each reference solution is obtained by solving the system that the surrogate is intended to replace.

By Ruifeng Cao (The University of Manchester), Xidan Song (Wuhan University)

rag

arXiv Machine Learning

Aug 7

Dual-space posterior sampling for Bayesian inference in constrained inverse problems

arXiv:2603. 00393v2 Announce Type: replace-cross Abstract: Inverse problems constrained by partial differential equations are often ill-conditioned due to noisy, incomplete data or inherent non-uniqueness.

By Ali Siahkoohi, Kamal Aghazade, Ali Gholami

benchmarks

arXiv Machine Learning

Jun 15

CANN-EUCLID: unsupervised constitutive artificial neural network model discovery from full-field data

arXiv:2606. 14565v1 Announce Type: cross Abstract: Constitutive artificial neural networks (CANNs) provide interpretable material model discovery, but have so far been used in stress-supervised settings based on apparent stress-strain data from homogeneous tests.

By Benjamin Alheit, Siddhant Kumar, Mathias Peirlinck

efficiency benchmarks

arXiv Machine Learning

Jul 13

Learning Physics-Informed Surrogate Model of Linear Elastic Displacement Fields from Geometry

arXiv:2607. 09382v1 Announce Type: new Abstract: This work aims to develop a fast and physically consistent surrogate model for real-time structural health monitoring of fractured elastic domains.

By Rodolphe Barlogis, Ferhat Tamssaouet, Quentin Falcoz, St\'ephane Grieu

arXiv Machine Learning

2d ago

Priors learned from legacy reconstructions inherit undetectable overconfidence

arXiv:2607. 21721v3 Announce Type: replace-cross Abstract: Where truths are scarce (e.

By Ali Siahkoohi, Sina Alemohammad

arXiv Machine Learning

Jun 8

No-Harm Physics-Informed Inverse Learning with Residual-Calibrated Uncertainty

arXiv:2606. 07153v1 Announce Type: cross Abstract: Physics-informed learning is increasingly used for partial differential equation (PDE)-governed inverse problems, but its reliability remains difficult to certify.

By Ronald Katende

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Discrete energy as an exact label-free training objective for finite-element surrogates

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