Results 41 to 50 of about 454 (120)

Pathogenesis of Tuberculosis: Interplay Between Host Antituberculosis Immunity and Immune Evasion Strategies of Mycobacterium Tuberculosis

MedComm, Volume 7, Issue 8, August 2026.
Mycobacterium tuberculosis–host interactions and infection outcomes. Following initial infection, alveolar macrophages phagocytose the pathogen, prompting the recruitment of innate immune cells including neutrophils, NK cells, and interstitial macrophages.
Xiaojuan Zhang, Ying Liu, Yilin Hou, Hongxia Niu   +3 more
wiley   +1 more source

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

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

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

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

A Kolmogorov–Arnold Surrogate Model for Chemical Equilibria: Application to Solid Solutions

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract The computational cost of geochemical solvers is a challenging matter. For reactive transport simulations, where chemical calculations are performed up to billions of times, it is crucial to reduce the total computational time. Existing publications have explored various machine learning approaches to determine the most effective data‐driven ...
Leonardo Boledi, Dirk Bosbach, Jenna Poonoosamy   +2 more
wiley   +1 more source