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Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions

Treur, Jan

1st ed. 2016., 2016

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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Journal of computational physics, 2019-02, Vol.378, p.686-707

2019

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Finite-Horizon Control-Constrained Nonlinear Optimal Control Using Single Network Adaptive Critics

IEEE transaction on neural networks and learning systems, 2013-01, Vol.24 (1), p.145-157

2013

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IEEE_Xplore

Developing a Local Least-Squares Support Vector Machines-Based Neuro-Fuzzy Model for Nonlinear and Chaotic Time Series Prediction

IEEE transaction on neural networks and learning systems, 2013-02, Vol.24 (2), p.207-218

2013

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IEEE_Xplore

A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials

Journal of computational physics, 2017-02, Vol.330 (C), p.192-220

2017

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Linear, first and second-order, unconditionally energy stable numerical schemes for the phase field model of homopolymer blends

Journal of computational physics, 2016-12, Vol.327, p.294-316

Yang, Xiaofeng

2016

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Contact-Force Distribution Optimization and Control for Quadruped Robots Using Both Gradient and Adaptive Neural Networks

IEEE transaction on neural networks and learning systems, 2014-08, Vol.25 (8), p.1460-1473

2014

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IEEE_Xplore

A family of high-order targeted ENO schemes for compressible-fluid simulations

Journal of computational physics, 2016-01, Vol.305, p.333-359

2016

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Atomic cluster expansion: Completeness, efficiency and stability

Journal of computational physics, 2022-04, Vol.454, p.110946, Article 110946

2022

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Measuring and moderating opinion polarization in social networks

Data mining and knowledge discovery, 2017-09, Vol.31 (5), p.1480-1505

2017

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Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations

Journal of chemical information and modeling, 2021-09, Vol.61 (9), p.4521-4536

2021

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Three naive Bayes approaches for discrimination-free classification

Data mining and knowledge discovery, 2010-09, Vol.21 (2), p.277-292

2010

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Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction

IEEE transaction on neural networks and learning systems, 2023-07, Vol.34 (7), p.3357-3370

2023

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IEEE_Xplore

Approximating the partition function of the ferromagnetic Potts model

Journal of the ACM, 2012-10, Vol.59 (5), p.1-31

2012

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems

Journal of computational physics, 2020-07, Vol.413, p.109456, Article 109456

2020

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Machine learning strategies for systems with invariance properties

Journal of computational physics, 2016-08, Vol.318 (C), p.22-35

2016

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Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding

IEEE transaction on neural networks and learning systems, 2024-04, Vol.35 (4), p.4948-4962

2024

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IEEE_Xplore

Physics-Constraint Variational Neural Network for Wear State Assessment of External Gear Pump

IEEE transaction on neural networks and learning systems, 2024-05, Vol.35 (5), p.5996-6006

2024

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IEEE_Xplore

Adversarial uncertainty quantification in physics-informed neural networks

Journal of computational physics, 2019-10, Vol.394 (C), p.136-152

2019

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Simulation of hyperelastic materials in real-time using deep learning

Medical image analysis, 2020-01, Vol.59, p.101569-101569, Article 101569

2020

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Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers

Journal of computational physics, 2020-04, Vol.406, p.109216, Article 109216

2020

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SPH accuracy improvement through the combination of a quasi-Lagrangian shifting transport velocity and consistent ALE formalisms

Journal of computational physics, 2016-05, Vol.313, p.76-98

2016

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Arbitrarily high-order linear energy stable schemes for gradient flow models

Journal of computational physics, 2020-10, Vol.419, p.109610, Article 109610

2020

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Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification

Journal of computational physics, 2018-12, Vol.375, p.565-588

2018

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Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Finite-Horizon Control-Constrained Nonlinear Optimal Control Using Single Network Adaptive Critics

Developing a Local Least-Squares Support Vector Machines-Based Neuro-Fuzzy Model for Nonlinear and Chaotic Time Series Prediction

A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials

Linear, first and second-order, unconditionally energy stable numerical schemes for the phase field model of homopolymer blends

Contact-Force Distribution Optimization and Control for Quadruped Robots Using Both Gradient and Adaptive Neural Networks

Recent developments in the ABINIT software package

A family of high-order targeted ENO schemes for compressible-fluid simulations

Atomic cluster expansion: Completeness, efficiency and stability

Measuring and moderating opinion polarization in social networks

Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations

Three naive Bayes approaches for discrimination-free classification

Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction

Approximating the partition function of the ferromagnetic Potts model

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems

Machine learning strategies for systems with invariance properties

Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding

Physics-Constraint Variational Neural Network for Wear State Assessment of External Gear Pump

Adversarial uncertainty quantification in physics-informed neural networks

Simulation of hyperelastic materials in real-time using deep learning

Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers

SPH accuracy improvement through the combination of a quasi-Lagrangian shifting transport velocity and consistent ALE formalisms

Arbitrarily high-order linear energy stable schemes for gradient flow models

Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification

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