Results 201 to 210 of about 855 (241)

Planar hyperbolic polaritons in 2D van der Waals materials. [PDF]

Nat Commun
Wang H, Kumar A, Dai S, Lin X, Jacob Z, Oh SH, Menon V, Narimanov E, Kim YD, Kim YD, Wang JP, Avouris P, Martin Moreno L, Caldwell J, Low T.   +14 more
europepmc   +1 more source

A trust‐region funnel algorithm for gray‐box optimization

AIChE Journal, EarlyView.
Abstract Gray‐box optimization, where parts of optimization problems are represented by algebraic models while others are treated as black‐box models lacking analytic derivatives, remains a challenge. Trust‐region (TR) methods provide a robust framework for gray‐box problems through local reduced models (RMs) for black‐box components, but they are ...
Gul Hameed, Tao Chen, Antonio del Rio Chanona, Lorenz T. Biegler, Michael Short   +4 more
wiley   +1 more source

Lorentzian bordisms in algebraic quantum field theory. [PDF]

Lett Math Phys
Bunk S, MacManus J, Schenkel A.
europepmc   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Exact hydrodynamic manifolds for the linear Boltzmann BGK equation I: spectral theory. [PDF]

Contin Mech Thermodyn
Kogelbauer F, Karlin I.
europepmc   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Understanding Nanoparticle Electronic Spin-State Dynamics and Properties Using Variable-Temperature, Variable-Field Magnetic Circular Photoluminescence. [PDF]

Chemphyschem
Knappenberger JA, Knappenberger KL.
europepmc   +1 more source

Mixed problems for degenerate hyperbolic equations of second order

Proceedings of the Japan Academy, Series A, Mathematical Sciences, 1969
openaire   +2 more sources

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

Optical Spin Angular Momentum: Properties, Topologies, Detection and Applications. [PDF]

Nanomaterials (Basel)
Liu S, Xie X, Shi P, Shen Y.
europepmc   +1 more source