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A classification-pursuing adaptive approach for Gaussian process regression on unlabeled data

Mechanical systems and signal processing, 2022-01, Vol.162, p.107976, Article 107976

2022

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On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling

Computer methods in applied mechanics and engineering, 2022-05, Vol.394, p.114915, Article 114915

2022

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The mixed Deep Energy Method for resolving concentration features in finite strain hyperelasticity

Journal of computational physics, 2022-02, Vol.451, p.110839, Article 110839

2022

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Enhancing high-fidelity nonlinear solver with reduced order model

Scientific reports, 2022-11, Vol.12 (1), p.20229-15, Article 20229

2022

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Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks

Computer methods in applied mechanics and engineering, 2022-01, Vol.388, p.114217, Article 114217

2022

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State-of-the-Art and Comparative Review of Adaptive Sampling Methods for Kriging

Archives of computational methods in engineering, 2021-06, Vol.28 (4), p.2689-2747

2021

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Interval and fuzzy physics-informed neural networks for uncertain fields

Probabilistic engineering mechanics, 2022-04, Vol.68, p.103240, Article 103240

2022

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Surrogate model approach for investigating the stability of a friction-induced oscillator of Duffing’s type

Nonlinear dynamics, 2019-11, Vol.98 (3), p.1709-1729

2019

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PI/PID controller stabilizing sets of uncertain nonlinear systems: an efficient surrogate model-based approach

Nonlinear dynamics, 2021-07, Vol.105 (1), p.277-299

2021

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Polyconvex neural network models of thermoelasticity

Journal of the mechanics and physics of solids, 2024-11, Vol.192, p.105837, Article 105837

2024

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Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger–Rivlin–Ericksen

Journal of computing and information science in engineering, 2024-11, Vol.24 (11)

2024

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Efficient surrogate strategy for investigating stick‐slip instability

Proceedings in applied mathematics and mechanics, 2021-12, Vol.21 (1), p.n/a

2021

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Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations

Materialia, 2022-06, Vol.23, p.101446, Article 101446

2022

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Establishing the relationship between generalized crystallographic texture and macroscopic yield surfaces using partial input convex neural networks

Materialia, 2024-08, Vol.36, p.102151, Article 102151

2024

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A classification-pursuing adaptive approach for Gaussian process regression on unlabeled data

On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling

The mixed Deep Energy Method for resolving concentration features in finite strain hyperelasticity

Enhancing high-fidelity nonlinear solver with reduced order model

Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks

State-of-the-Art and Comparative Review of Adaptive Sampling Methods for Kriging

Interval and fuzzy physics-informed neural networks for uncertain fields

Surrogate model approach for investigating the stability of a friction-induced oscillator of Duffing’s type

PI/PID controller stabilizing sets of uncertain nonlinear systems: an efficient surrogate model-based approach

Polyconvex neural network models of thermoelasticity

Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger–Rivlin–Ericksen

Efficient surrogate strategy for investigating stick‐slip instability

Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations

Establishing the relationship between generalized crystallographic texture and macroscopic yield surfaces using partial input convex neural networks

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