Results 71 to 80 of about 1,373 (161)

Nordgren PINNs to VQE: Advancing Hydraulic Fracturing Simulations in Shale Reservoirs

International Journal for Numerical and Analytical Methods in Geomechanics, Volume 50, Issue 12, Page 4863-4878, 25 August 2026.
ABSTRACT This study advances hydraulic fracturing simulations in shale reservoirs using two computational paradigms, Physics‐Informed Neural Networks (PINNs) and the Variational Quantum Eigensolver (VQE). PINNs were employed to solve Nordgren's equation, which governs fracture width evolution, by embedding physical laws into the neural network ...
Dennis Delali Kwesi Wayo, Sonny Irawan, Mohd Zulkifli Bin Mohamad Noor, Mudasar Zafar, Shynar Juziyeva, Camila Martins Saporetti, Leonardo Goliatt, Randy Hazlett   +7 more
wiley   +1 more source

Efficient Tensor Completion Algorithms for Highly Oscillatory Operators

Numerical Linear Algebra with Applications, Volume 33, Issue 4, August 2026.
ABSTRACT We address the problem of recovering highly oscillatory operators, represented as n×n$$ n\times n $$ matrices with a fixed set of observed entries. Given that these matrices can be well compressed by butterfly matrix decomposition of L=𝒪(logn) levels requiring only O(nlogn)$$ O\left(n\log n\right) $$ degrees of freedom, we propose a novel ...
Navjot Singh, Edgar Solomonik, Xiaoye Sherry Li, Yang Liu   +3 more
wiley   +1 more source

Spectral Analysis of Block Diagonally Preconditioned Multiple Saddle‐Point Matrices With Inexact Schur Complements

Numerical Linear Algebra with Applications, Volume 33, Issue 4, August 2026.
ABSTRACT We derive eigenvalue bounds for symmetric block‐tridiagonal multiple saddle‐point systems preconditioned with block‐diagonal Schur complement matrices. This analysis applies to an arbitrary number of blocks and accounts for the case where the Schur complements are approximated, generalizing the findings in [11, Bergamaschi et al., Linear ...
Marco Pilotto, Luca Bergamaschi, Ángeles Martínez   +2 more
wiley   +1 more source

Synergistic Engineering of Nanostructures via Anodic Aluminum Oxide Templates and Atomic Layer Deposition: Design Principles, Mechanisms, and Applications

Small Structures, Volume 7, Issue 8, August 2026.
Anodic aluminum oxide (AAO) templates combined with atomic layer deposition (ALD) constitute a synergistic platform for engineering functional nanostructures within highly ordered, high‐aspect‐ratio porous architectures. By linking precursor transport modeling, surface chemistry control, and tailored ALD strategies, this review establishes a unified ...
Hyeon Joon Choi, Neul Ha, Jisu Jung, Ryeowon Kim, S. Joon Kwon, Sunghak Park, Wooseok Yang, Hyunjung Shin   +7 more
wiley   +1 more source

Data-driven, ML-assisted approaches to problem well-posedness. [PDF]

PNAS Nexus
Bertalan T, Kevrekidis GA, Koronaki ED, Mishra S, Rebrova E, Kevrekidis IG.   +5 more
europepmc   +1 more source

Structure–Property Relationships of Atmospheric Water Harvesting for Water‐Carbon‐Energy Sustainability

Small Structures, Volume 7, Issue 8, August 2026.
This review discusses the integration of advanced characterization, computational modeling, and multifunctional atmospheric water harvesting (AWH) systems for sustainable water‐energy‐carbon solutions. These methods reveal structure‐property relationships, guiding next‐generation hygroscopic material design.
Miao Tang, Huan Liu, Mitch Guijun Li
wiley   +1 more source

Fast automated adjoints for spectral PDE solvers. [PDF]

Proc Natl Acad Sci U S A
Skene CS, Burns KJ.
europepmc   +1 more source

Abstracts

Molecular Oncology, Volume 20, Issue S1, Page 1-692, August 2026.
Abstracts submitted to the ‘EACR 2026 Congress: Innovative Cancer Science’, from 08–11 June 2026 and accepted by the Congress Organising Committee are published in this Supplement of Molecular Oncology, an affiliated journal of the European Association for Cancer Research (EACR).
wiley   +1 more source

Gradient‐Free Online Learning of Subgrid‐Scale Dynamics With Neural Emulators

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 8, August 2026.
Abstract In this paper, we propose a generic algorithm to train machine learning‐based subgrid parametrizations online, that is, with a posteriori loss functions, but for non‐differentiable numerical solvers. The proposed approach leverages a neural emulator to approximate the reduced state‐space solver, which is then used to allow gradient propagation
H. Frezat, R. Fablet, G. Balarac, J. Le Sommer   +3 more
wiley   +1 more source

The finite element neural network method to simulate two dimensional partial differential equations and perform parameter identification. [PDF]

Sci Rep
Abda M, Berthet L, Hamedi M, Hibatullah D, Piollet E, Blake C, Blais B, Gosselin FP.   +7 more
europepmc   +1 more source