Bouklas, Nikolaos

1. A review on data-driven constitutive laws for solids
   2024/05/06 by Jan N. Fuhg, Fuhg, Jan Niklas, Govinda Anantha Padmanabha +15 · 30 citations
   Engineering · #74-02 (Primary) #Advanced machining processes and optimization #Applied Physics (physics.app-ph) #Computational Engineering #Elasticity and Material Modeling #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Machine Learning (cs.LG) #Metallurgy and Material Forming #and Science (cs.CE)
2. A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
   2021/05/27 by Teeratorn Kadeethum, Kadeethum, Teeratorn, Daniel O’Malley +11 · 5 citations
   Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Generative Adversarial Networks and Image Synthesis #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Numerical Analysis (math.NA)
3. Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics
   2023/10/05 by Jan N. Fuhg, Reese E. Jones, Fuhg, Jan N. +3 · 8 citations
   Physics and Astronomy · Engineering · Medicine · #Model Reduction and Neural Networks #Elasticity and Material Modeling #Orthopaedic implants and arthroplasty
4. Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials
   2023/04/27 by Kshitiz Upadhyay, Jan N. Fuhg, Upadhyay, Kshitiz +5 · 5 citations
   Engineering · Materials Science · Physics and Astronomy · #Computational Engineering #Elasticity and Material Modeling #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Model Reduction and Neural Networks #Soft Condensed Matter (cond-mat.soft) #and Science (cs.CE)
5. Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations
   2022/01/28 by Fuhg, Jan N., van Wees, Lloyd, Obstalecki, Mark +3 · 4 citations
   #68T07 (Secondary) #74C15 #74Q15(Primary) #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci)
6. Interval and fuzzy physics-informed neural networks for uncertain fields
   2021/06/18 by Jan N. Fuhg, Ioannis Kalogeris, Fuhg, Jan Niklas +5 · 2 citations
   Computer Science · Physics and Astronomy · #35Q74 #Computational Engineering #FOS: Computer and information sciences #FOS: Mathematics #Finance #I.2.8 #J.2 #Machine Learning (cs.LG) #Machine Learning and ELM #Model Reduction and Neural Networks #Neural Networks and Applications #Numerical Analysis (math.NA) #and Science (cs.CE)
7. Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen
   2023/08/21 by Jan N. Fuhg, Nikolaos Bouklas, Fuhg, Jan N. +3 · 2 citations
   Engineering · Mathematics · Physics and Astronomy · #Elasticity and Material Modeling #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Soft Condensed Matter (cond-mat.soft) #Tensor decomposition and applications
8. Multiscale simulation of spatially correlated microstructure via a latent space representation
   2024/05/29 by Jones, Reese E., Hamel, Craig M., Bolintineanu, Dan +5 · 2 citations
   #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci)
9. Deep Convolutional Ritz Method: Parametric PDE surrogates without labeled data
   2022/06/07 by Fuhg, Jan Niklas, Karmarkar, Arnav, Kadeethum, Teeratorn +2 · 1 citation
   #35Q68 (Secondary) #65N99 (Primary) 35Q62 #Computational Engineering #FOS: Computer and information sciences #Finance #G.1.8 #and Science (cs.CE)
10. Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
    2024/06/30 by Govinda Anantha Padmanabha, Padmanabha, Govinda Anantha, Jan N. Fuhg +7 · 2 citations
    Physics and Astronomy · Computer Science · Earth and Planetary Sciences · #Model Reduction and Neural Networks #Domain Adaptation and Few-Shot Learning #Seismic Imaging and Inversion Techniques