Results 81 to 90 of about 2,048 (231)
An analysis of constraint-relaxation in PDE-based inverse problems
Inverse ProblemsAbstract Many inverse problems are naturally formulated as a PDE-constrained optimization problem. These non-linear, large-scale, constrained optimization problems know many challenges, of which the inherent non-linearity of the problem is an important one.
T. van Leeuwen (Tristan) +1 more
openaire +2 more sources
Nordgren PINNs to VQE: Advancing Hydraulic Fracturing Simulations in Shale Reservoirs
International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.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 +7 more
wiley +1 more source
Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems Using Transfer Learning
Applied SciencesPhysics-Informed Neural Network (PINN) is a data-driven solver for partial and ordinary differential equations (ODEs/PDEs). It provides a unified framework to address both forward and inverse problems.
Abdul Hannan Mustajab +3 more
doaj +1 more source
Coexistence, crossover and extirpation in coalescent communities and ecotones
Oikos, EarlyView.When two ecological communities come into contact, the strength of their mixing determines whether species coexist, extirpate, or extend their ranges. We present analytical formulas and simulations describing these transitions. Specifically, we derive abundance shifts upon community coalescence, identify the critical mixing strength leading to first ...
Martin Heidelman, Dervis Can Vural
wiley +1 more source
The Hadamard-PINN for PDE inverse problems: Convergence with distant initial guesses
Examples and CounterexamplesThis paper presents the Hadamard-Physics-Informed Neural Network (H-PINN) for solving inverse problems in partial differential equations (PDEs), specifically the heat equation and the Korteweg–de Vries (KdV) equation.
Yohan Chandrasukmana +2 more
doaj +1 more source
Micro‐Mechanism Informed Neural Networks for Process‐Property Prediction in Laser Powder Bed Fusion
Artificial Intelligence for Engineering, EarlyView.Hard physics embedding, where neural networks learn residuals relative to analytical baselines, substantially outperforms soft loss‐function constraints for extrapolation in LPBF process–property prediction. Physics integration architecture determines generalization capability more than constraint quantity.
Yo‐Lun Yang
wiley +1 more source
A General Method for the Solution of Inverse Problems in Transport Phenomena
Chemical Engineering Transactions, 2015 The typical inverse problems in transport phenomena are given by partial differential equations with unknown boundary conditions, which are to be estimated from measurements corresponding to solutions of the PDEs or of their gradients.
M. Vocciante, A. Reverberi, V. Dovi
doaj +1 more source
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
wiley +1 more source
Efficient MCMC and posterior consistency for Bayesian inverse problems [PDF]
Many mathematical models used in science and technology often contain parameters that are not known a priori. In order to match a model to a physical phenomenon, the parameters have to be adapted on the basis of the available data.
Vollmer, Sebastian
core
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
wiley +1 more source