References in zbMATH (referenced in 23 articles )

Showing results 1 to 20 of 23.
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  1. Andrew Engel, Zhichao Wang, Anand D. Sarwate, Sutanay Choudhury, Tony Chiang: TorchNTK: A Library for Calculation of Neural Tangent Kernels of PyTorch Models (2022) arXiv
  2. Bezgin, Deniz A.; Schmidt, Steffen J.; Adams, Nikolaus A.: WENO3-NN: a maximum-order three-point data-driven weighted essentially non-oscillatory scheme (2022)
  3. Boureima, I.; Gyrya, V.; Saenz, J. A.; Kurien, S.; Francois, M.: Dynamic calibration of differential equations using machine learning, with application to turbulence models (2022)
  4. Fokina, Daria; Iliev, Oleg; Oseledets, Ivan: Deep neural networks and adaptive quadrature for solving variational problems (2022)
  5. Huang, Daniel; Teng, Chong; Bao, Junwei Lucas; Tristan, Jean-Baptiste: mad-GP: automatic differentiation of Gaussian processes for molecules and materials (2022)
  6. Paul Scherer, Thomas Gaudelet, Alison Pouplin, Suraj M S, Jyothish Soman, Lindsay Edwards, Jake P. Taylor-King: PyRelationAL: A Library for Active Learning Research and Development (2022) arXiv
  7. Sukumar, N.; Srivastava, Ankit: Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (2022)
  8. Zhang, Wenbo; Li, David S.; Bui-Thanh, Tan; Sacks, Michael S.: Simulation of the 3D hyperelastic behavior of ventricular myocardium using a finite-element based neural-network approach (2022)
  9. Anderson, Lara B.; Gerdes, Mathis; Gray, James; Krippendorf, Sven; Raghuram, Nikhil; Ruehle, Fabian: Moduli-dependent Calabi-Yau and SU(3)-structure metrics from machine learning (2021)
  10. de Felice, Giovanni; Toumi, Alexis; Coecke, Bob: DisCoPy: monoidal categories in Python (2021)
  11. Frank Schäfer, Mohamed Tarek, Lyndon White, Chris Rackauckas: AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia (2021) arXiv
  12. Johnston, Hunter; Mortari, Daniele: Least-squares solutions of boundary-value problems in hybrid systems (2021)
  13. Li, Zhihan; Fan, Yuwei; Ying, Lexing: Multilevel fine-tuning: closing generalization gaps in approximation of solution maps under a limited budget for training data (2021)
  14. Lukas Heinrich; Matthew Feickert; Giordon Stark; Kyle Cranmer: pyhf: pure-Python implementation of HistFactory statistical models (2021) not zbMATH
  15. Lukas Prediger, Niki Loppi, Samuel Kaski, Antti Honkela: d3p - A Python Package for Differentially-Private Probabilistic Programming (2021) arXiv
  16. Schoenholz, Samuel S.; Cubuk, Ekin D.: JAX, M.D. a framework for differentiable physics (2021)
  17. Wang, Sifan; Wang, Hanwen; Perdikaris, Paris: On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks (2021)
  18. Bobev, Nikolay; Fischbacher, Thomas; Gautason, Fridrik Freyr; Pilch, Krzysztof: A cornucopia of (\mathrmAdS_5) vacua (2020)
  19. Joshua G. Albert: JAXNS: a high-performance nested sampling package based on JAX (2020) arXiv
  20. Laue, Sören; Mitterreiter, Matthias; Giesen, Joachim: A simple and efficient tensor calculus for machine learning (2020)

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