Search Results - Physics-informed greedy algorithm

gLaSDI: Parametric physics-informed greedy latent space dynamics identification by He, Xiaolong, Choi, Youngsoo, Fries, William D., Belof, Jonathan L., Chen, Jiun-Shyan

“… reduced-order modeling. To maximize and accelerate the exploration of the parameter space for the optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed residual…”
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Greedy Algorithms for Physics-Informed Sparse Sensor Selection by Clark, Emily E

“… Instead, researchers have developed techniques to calculate near-optimal sensor placements, usually based on convex relaxations or greedy algorithms…”
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gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification by He, Xiaolong, Choi, Youngsoo, Fries, William D, Belof, Jon, Chen, Jiun-Shyan

“… reduced-order modeling. To maximize and accelerate the exploration of the parameter space for the optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed residual…”
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Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence by Yang, Xiu, Barajas-Solano, David, Tartakovsky, Guzel, Tartakovsky, Alexandre M.

“…: physics-informed CoKriging (CoPhIK). In CoKriging-based multifidelity methods, the quantities of interest are modeled as linear combinations of multiple parameterized stationary Gaussian processes (GPs…”
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Certified data-driven physics-informed greedy auto-encoder simulator by He, Xiaolong, Choi, Youngsoo, Fries, William D, Belof, Jonathan L, Chen, Jiun-Shyan

“… To effectively explore the parameter space for optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed error indicator is introduced to search for optimal…”
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An algorithm for physics informed scan path optimization in additive manufacturing by Stump, B.

“…•Generator algorithm for creating a variety of scan paths.•Results show ability for fine control of site-specific microstructure…”
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Generative point sampling strategies for physics-informed neural networks by Kochliaridis, Vasileios, Dilmperis, Ioannis, Palaskos, Achilleas, Vlahavas, Ioannis

“…Physics-Informed Neural Networks (PINNs) have been a groundbreaking approach for solving complex boundary-value systems using Neural Networks…”
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Physics-Aware Neural Networks for Distribution System State Estimation by Zamzam, Ahmed Samir, Sidiropoulos, Nicholas D.

“…The distribution system state estimation problem seeks to determine the network state from available measurements. Widely used Gauss-Newton approaches are very…”
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Thermodynamically Consistent Physics-Informed Data-Driven Computing and Reduced-Order Modeling of Nonlinear Materials by He, Xiaolong

“…Physical simulations have influenced the advancements in engineering, technology, and science more rapidly than ever before. However, it remains challenging…”
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Gauss Newton Method for Solving Variational Problems of PDEs with Neural Network Discretizaitons by Hao, Wenrui, Hong, Qingguo, Jin, Xianlin

“… Various approaches, such as the deep Ritz method and physics-informed neural networks, have been developed for numerical solutions…”
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Physics-informed data-driven Bayesian network for the risk analysis of hydrogen refueling stations by Xing, Jinduo, Qian, Jiaqi, Peng, Rui, Zio, Enrico

“…The safety of hydrogen refueling stations (HRSs) is receiving increasing attention with the growth use of hydrogen energy. Existing risk assessment methods of…”
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GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs by Chen, Yanlai, Koohy, Shawn

“…Physics-Informed Neural Network (PINN) has proven itself a powerful tool to obtain the numerical solutions of nonlinear partial differential equations (PDEs…”
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POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier–Stokes equations by Hijazi, Saddam, Freitag, Melina, Landwehr, Niels

“…We present a Reduced Order Model (ROM) which exploits recent developments in Physics Informed Neural Networks (PINNs…”
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Physics-Informed Graph Capsule Generative Autoencoder for Probabilistic AC Optimal Power Flow by Saffari, Mohsen, Khodayar, Mahdi, Khodayar, Mohammad E.

“…Due to the increasing demand for electricity and the inherent uncertainty in power generation, finding efficient solutions to the stochastic alternating…”
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Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence by Yang, Xiu, Barajas-Solano, David, Tartakovsky, Guzel, Tartakovsky, Alexandre

“…: physics-informed CoKriging (CoPhIK). In CoKriging-based multifidelity methods, the quantities of interest are modeled as linear combinations of multiple parameterized stationary Gaussian processes (GPs…”
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Greedy training algorithms for neural networks and applications to PDEs by Siegel, Jonathan W., Hong, Qingguo, Jin, Xianlin, Hao, Wenrui, Xu, Jinchao

“… It is our goal in this work to take a step toward remedying this. For this purpose, we develop a novel greedy training algorithm for shallow neural networks…”
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Gauss Newton method for solving variational problems of PDEs with neural network discretizaitons by Hao, Wenrui, Hong, Qingguo, Jin, Xianlin

“… Various approaches, such as the deep Ritz method and physics-informed neural networks, have been developed for numerical solutions…”
Get full text

Greedy Training Algorithms for Neural Networks and Applications to PDEs by Siegel, Jonathan W, Hong, Qingguo, Jin, Xianlin, Hao, Wenrui, Xu, Jinchao

“… It is our goal in this work to take a step toward remedying this. For this purpose, we develop a novel greedy training algorithm for shallow neural networks…”
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GS-PINN: Greedy Sampling for Parameter Estimation in Partial Differential Equations by ootani, Ali, Kapadia, Harshit, Chellappa, Sridhar, Goyal, Pawan, Benner, Peter

“… equation to estimate its parameters. Greedy samples are used to train a physics-informed neural network architecture which maps the nonlinear relation between spatio-temporal data and the measured values…”
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Online sequential decision making of multi-stage assembly process parameters based on deep reinforcement learning and its application in diesel engine production by Song, Yi-Tian, Sun, Yan-Ning, Liu, Li-Lan, Wu, Jie, Gao, Zeng-Gui, Qin, Wei

“…Maintaining fixed parameters during batch assembly of complex mechanical products often results in quality inconsistencies due to time-varying operational…”
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