Results 41 to 50 of about 281 (113)

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

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

Fine‐Tuning a Weather Foundation Model With Lightweight Decoders for Unseen Physical Processes

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Recent advances in AI weather forecasting have led to the emergence of so‐called “foundation models”, typically defined by expensive pretraining and minimal fine‐tuning for downstream tasks. However, in the natural sciences, a desirable foundation model should also encode meaningful statistical relationships between the underlying physical ...
Fanny Lehmann, Firat Ozdemir, Benedikt Soja, Torsten Hoefler, Siddhartha Mishra, Sebastian Schemm   +5 more
wiley   +1 more source

Modeling Transient Flow in Heterogeneous Aquifers With the Mixed Pressure‐Velocity Formulation of Physics Informed Neural Networks

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Physics‐Informed Neural Networks (PINNs) have emerged as a powerful framework for modeling groundwater flow using deep learning neural networks, particularly in scenarios where traditional data‐driven approaches are limited by the scarcity of data.
Adhish Virupaksha, Marwan Fahs, Hussein Hoteit, Thomas Nagel, François Lehmann   +4 more
wiley   +1 more source

Learning 2D Shallow Water Equations With Physics‐Informed Neural Operator Networks

Water Resources Research, Volume 62, Issue 8, August 2026.
Abstract This study investigates the application of Physics‐Informed Neural Operators (PINOs) for solving the two‐dimensional shallow water equations (2D SWE) in the context of flood modeling. Unlike Physics‐Informed Neural Networks (PINNs), which require retraining for each new initial or boundary condition (BC), PINOs learn the solution operator ...
Robert Keppler, Rasmus Fensholt, Julian Koch   +2 more
wiley   +1 more source

On the use of tent spaces for solving PDEs: A proof of the Koch-Tataru theorem

Lecture notes with complete detailsInternational audienceIn these notes we will present (a part of) the parabolic tent spaces theory and then apply it in solving some PDE's originated from the fluid mechanics.
Vasilyev, Ioann, Auscher, Pascal
core  

A Hybrid ML‐PDE Framework for Predicting Breaking Ocean Waves

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Wave breaking plays a central role in ocean dynamics, dissipating wave energy and shaping the evolution of the sea surface. Yet, breaking remains difficult to model: envelope‐based models efficiently capture nonlinear wave evolution and are interpretable but exclude breaking, while high‐fidelity direct numerical simulations resolve breaking ...
Y. Liu, T. A. A. Adcock, T. S. van den Bremer, D. Eeltink   +3 more
wiley   +1 more source

Physics‐Informed Machine Learning Framework to Retroactively Estimate Mantle Thermal Convection From Partial Geophysical Observations

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Mantle convection drives the solid Earth, powering plate motions, volcanism, and earthquakes while regulating planetary heat loss. Reconstructing its history is hampered by sparse, noisy observations concentrated near the surface and the present day. Here I develop an inverse physics‐informed neural network framework to estimate mantle thermal
Atsushi Nakao
wiley   +1 more source