Results 61 to 70 of about 1,339 (124)

A Hierarchical Deep Symbolic AI Method for Closed‐Form, Interpretable Constitutive Modeling

open access: yesInternational Journal for Numerical Methods in Engineering, Volume 127, Issue 14, 30 July 2026.
ABSTRACT Recent advances in advanced manufacturing have enabled the development of multifunctional materials such as soft composites, whose complex nonlinear behaviors demand accurate and interpretable constitutive modeling. Traditional phenomenological formulations rely heavily on assumed functional forms and expert intuition, requiring extensive ...
Haozhe Yu   +3 more
wiley   +1 more source

Numerical Anisotropy and Band‐Gap Formation in Finite Element Wave Propagation: Analysis and Mitigation Using Spline‐Based Elements

open access: yesInternational Journal for Numerical Methods in Engineering, Volume 127, Issue 14, 30 July 2026.
ABSTRACT Numerical anisotropy and dispersion are inherent consequences of spatial discretization in finite element modeling of wave propagation. In two‐dimensional problems, these effects manifest not only as direction‐dependent phase and group velocities but also as angularly dependent numerical band gaps that may entirely suppress wave propagation ...
Wiktor Waszkowiak   +3 more
wiley   +1 more source

Style‐Constrained Inverse Design of Microstructures With Tailored Mechanical Properties Using Unconditional Diffusion Models

open access: yesInternational Journal for Numerical Methods in Engineering, Volume 127, Issue 13, 15 July 2026.
ABSTRACT Deep generative models, particularly denoising diffusion models, have achieved remarkable success in high‐fidelity generation of architected microstructures with desired properties and styles. However, these recent methods typically rely on conditional training mechanisms that require extensive labeled data.
Weipeng Xu   +5 more
wiley   +1 more source

A quantum-inspired classification for random mixed states. [PDF]

open access: yesSci Rep
Sergioli G   +6 more
europepmc   +1 more source

CAP: Commutative algebra prediction of protein-nucleic acid binding affinities. [PDF]

open access: yesMach Learn Sci Technol
Zia M   +5 more
europepmc   +1 more source

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