Results 131 to 140 of about 10,737 (259)

Dictionary‐based weak‐form training for noise‐robust series hybrid models with multiplicative unknowns

open access: yesAIChE Journal, EarlyView.
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho   +4 more
wiley   +1 more source

Solid particles focusing, orienting, and de‐focusing in planar Poiseuille flow

open access: yesAIChE Journal, EarlyView.
Abstract Three‐dimensional, time‐dependent lattice‐Boltzmann simulations of neutrally buoyant particles in planar Poiseuille flow have been performed. The particles are spherical or cylindrical (the latter with a length over diameter ratios in the range 1/3 to 2) and have volume fraction ranging from virtually zero (one particle) to 0.15. The channel's
J. J. Derksen
wiley   +1 more source

Evolution of Physical Intelligence Across Scales

open access: yesAdvanced Intelligent Discovery, EarlyView.
By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu   +7 more
wiley   +1 more source

Data‐Driven High‐Throughput Volume Fraction Estimation From X‐Ray Diffraction Patterns

open access: yesAdvanced Intelligent Discovery, EarlyView.
Long exposure times and the need for manual evaluation limit the use of X‐ray diffraction in high‐throughput applications. This study presents a data‐driven approach addressing both issues. HiVE (a method for High‐throughput Volume fraction Estimation) performs composition estimation for high‐noise XRD patterns produced using polychromatic emission ...
Hawo H. Höfer   +6 more
wiley   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

open access: yesAdvanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong   +5 more
wiley   +1 more source

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