Results 81 to 90 of about 35,012 (233)
Unsupervised Deep Learning Algorithm for PDE-based Forward and Inverse Problems [PDF]
, 2019 Leah Bar, Nir Sochen
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Multigrid Algorithms for Inverse Problems with Linear Parabolic PDE Constraints
SIAM Journal on Scientific Computing, 2008 We present a multigrid algorithm for the solution of source identification inverse problems constrained by variable-coefficient linear parabolic partial differential equations. We consider problems in which the inversion variable is a function of space only. We consider the case of $L^2$ Tikhonov regularization. The convergence rate of our algorithm is
Adavani, Santi S, Biros, George
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Quarterly Journal of the Royal Meteorological Society, EarlyView.This study presents improvements to the non‐hydrostatic version of the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), enabling stable global simulations at 1.4‐km resolution. A systematic comparison with the hydrostatic version at resolutions from 9 to 1.4 km shows that non‐hydrostatic effects emerge in ...
Jozef Vivoda +3 more
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Computing Skinning Weights via Convex Duality
Computer Graphics Forum, EarlyView.We present an alternate optimization method to compute bounded biharmonic skinning weights. Our method relies on a dual formulation, which can be optimized with a nonnegative linear least squares setup. Abstract We study the problem of optimising for skinning weights through the lens of convex duality.
J. Solomon, O. Stein
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Data-Centric EngineeringWe propose a physics-constrained convolutional neural network (PC-CNN) to solve two types of inverse problems in partial differential equations (PDEs), which are nonlinear and vary both in space and time.
Daniel Kelshaw, Luca Magri
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Mesh Processing Non‐Meshes via Neural Displacement Fields
Computer Graphics Forum, EarlyView.Abstract Mesh processing pipelines are mature, but adapting them to newer non‐mesh surface representations—which enable fast rendering with compact file size—requires costly meshing or transmitting bulky meshes, negating their core benefits for streaming applications.
Yuta Noma +4 more
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Building a Digital Twin for Material Testing: Model Reduction and Data Assimilation
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.ABSTRACT The rapid advancement of industrial technologies, data collection, and handling methods has paved the way for the widespread adoption of digital twins (DTs) in engineering, enabling seamless integration between physical systems and their virtual counterparts.
Rubén Aylwin +5 more
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A Novel Mixed‐Hybrid, Higher‐Order Accurate Formulation for Kirchhoff–Love Shells
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.ABSTRACT This paper presents a novel mixed‐hybrid finite element formulation for Kirchhoff–Love shells, designed to enable the use of standard C0$C^0$‐continuous higher‐order Lagrange elements. This is possible by introducing the components of the moment tensor as a primary unknown alongside the displacement vector, circumventing the need for C1$C^1 ...
Jonas Neumeyer, Thomas‐Peter Fries
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On the Performance and Convergence of PINNs for Problems in Linear Elasticity
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.ABSTRACT Physics‐informed neural networks (PINNs) have emerged as a promising approach for solving partial differential equations by embedding physical laws directly into the loss function. However, their performance characteristics for problems in computational mechanics remain insufficiently understood.
Dipraj Kadlag +3 more
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Estimates on the generalization error of Physics Informed Neural\n Networks (PINNs) for approximating a class of inverse problems for PDEs [PDF]
, 2020 Siddhartha Mishra, R. Molinaro
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