Nikolaos Bouklas

26 papers Journal 26

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Explosion-powered eversible tactile displays. Sci. Robotics Ronald H. Heisser, Khoi D. Ly, Ofek Peretz, Young S. Kim, Carlos A. Diaz-Ruiz, Rachel M. Miller, Cameron A. Aubin, Sadaf Sobhani, Nikolaos Bouklas, Robert F. Shepherd
2025	J	`jnl`	Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials. CoRR Steven Yang, Michal Levin, Govinda Anantha Padmanabha, Miriam Borshevsky, Ohad Cohen, D. Thomas Seidl, Reese E. Jones, Nikolaos Bouklas, Noy Cohen
2024	J	`jnl`	A review on data-driven constitutive laws for solids. CoRR Jan Niklas Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas, Bahador Bahmani, WaiChing Sun, Nikolaos N. Vlassis, Moritz Flaschel, Pietro Carrara, Laura De Lorenzis
2024	J	`jnl`	Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks. CoRR Govinda Anantha Padmanabha, Cosmin Safta, Nikolaos Bouklas, Reese E. Jones
2024	J	`jnl`	Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models. CoRR Govinda Anantha Padmanabha, Jan Niklas Fuhg, Cosmin Safta, Reese E. Jones, Nikolaos Bouklas
2024	J	`jnl`	Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger-Rivlin-Ericksen. J. Comput. Inf. Sci. Eng. Jan Niklas Fuhg, Nikolaos Bouklas, Reese E. Jones
2023	J	`jnl`	Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems. IEEE Access Teeratorn Kadeethum, John D. Jakeman, Youngsoo Choi, Nikolaos Bouklas, Hongkyu Yoon
2023	J	`jnl`	Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics. CoRR Jan Niklas Fuhg, Reese E. Jones, Nikolaos Bouklas
2023	J	`jnl`	NN-EVP: A physics informed neural network-based elasto-viscoplastic framework for predictions of grain size-aware flow response under large deformations. CoRR Adnan Eghtesad, Jan Niklas Fuhg, Nikolaos Bouklas
2023	J	`jnl`	Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials. CoRR Kshitiz Upadhyay, Jan Niklas Fuhg, Nikolaos Bouklas, K. T. Ramesh
2023	J	`jnl`	Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen. CoRR Jan Niklas Fuhg, Nikolaos Bouklas, Reese E. Jones
2022	J	`jnl`	Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. Comput. Geosci. Teeratorn Kadeethum, Daniel O'Malley, Youngsoo Choi, Hari S. Viswanathan, Nikolaos Bouklas, Hongkyu Yoon
2022	J	`jnl`	Deep Convolutional Ritz Method: Parametric PDE surrogates without labeled data. CoRR Jan Niklas Fuhg, Arnav Karmarkar, Teeratorn Kadeethum, Hongkyu Yoon, Nikolaos Bouklas
2022	J	`jnl`	Modular machine learning-based elastoplasticity: generalization in the context of limited data. CoRR Jan Niklas Fuhg, Craig M. Hamel, Kyle Johnson, Reese E. Jones, Nikolaos Bouklas
2022	J	`jnl`	Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds. CoRR Teeratorn Kadeethum, Francesco Ballarin, Daniel O'Malley, Youngsoo Choi, Nikolaos Bouklas, Hongkyu Yoon
2022	J	`jnl`	The mixed Deep Energy Method for resolving concentration features in finite strain hyperelasticity. J. Comput. Phys. Jan Niklas Fuhg, Nikolaos Bouklas
2021	J	`jnl`	A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks. CoRR Teeratorn Kadeethum, Daniel O'Malley, Jan Niklas Fuhg, Youngsoo Choi, Jonghyun Lee, Hari S. Viswanathan, Nikolaos Bouklas
2021	J	`jnl`	A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks. Nat. Comput. Sci. Teeratorn Kadeethum, Daniel O'Malley, Jan Niklas Fuhg, Youngsoo Choi, Jonghyun Lee, Hari S. Viswanathan, Nikolaos Bouklas
2021	J	`jnl`	Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. CoRR Teeratorn Kadeethum, Dan O'Malley, Youngsoo Choi, Hari S. Viswanathan, Nikolaos Bouklas, Hongkyu Yoon
2021	J	`jnl`	Interval and fuzzy physics-informed neural networks for uncertain fields. CoRR Jan Niklas Fuhg, Amelie Fau, Nikolaos Bouklas
2021	J	`jnl`	Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks. CoRR Jan Niklas Fuhg, Michele Marino, Nikolaos Bouklas
2021	J	`jnl`	Model-data-driven constitutive responses: application to a multiscale computational framework. CoRR Jan Niklas Fuhg, Christoph Böhm, Nikolaos Bouklas, Amelie Fau, Peter Wriggers, Michele Marino
2021	J	`jnl`	Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques. CoRR Teeratorn Kadeethum, Francesco Ballarin, Youngsoo Choi, Daniel O'Malley, Hongkyu Yoon, Nikolaos Bouklas
2021	J	`jnl`	Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation. CoRR Teeratorn Kadeethum, Francesco Ballarin, Nikolaos Bouklas
2021	J	`jnl`	On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling. CoRR Jan Niklas Fuhg, Nikolaos Bouklas
2021	J	`jnl`	The mixed deep energy method for resolving concentration features in finite strain hyperelasticity. CoRR Jan Niklas Fuhg, Nikolaos Bouklas