JAX
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Software:44707
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Source code repository: https://github.com/google/jax
Related Items (41)
A Model-Constrained Tangent Slope Learning Approach for Dynamical Systems ⋮ Statistical Finite Elements via Langevin Dynamics ⋮ Discovering faster matrix multiplication algorithms with reinforcement learning ⋮ Optimal Transport for Parameter Identification of Chaotic Dynamics via Invariant Measures ⋮ Deep neural networks and adaptive quadrature for solving variational problems ⋮ Learning probabilistic termination proofs ⋮ WENO3-NN: a maximum-order three-point data-driven weighted essentially non-oscillatory scheme ⋮ Dynamic calibration of differential equations using machine learning, with application to turbulence models ⋮ Simulation of the 3D hyperelastic behavior of ventricular myocardium using a finite-element based neural-network approach ⋮ mad-GP: automatic differentiation of Gaussian processes for molecules and materials ⋮ Improved architectures and training algorithms for deep operator networks ⋮ Data-driven tissue mechanics with polyconvex neural ordinary differential equations ⋮ Autodifferentiable Ensemble Kalman Filters ⋮ Automated Reinforcement Learning (AutoRL): A Survey and Open Problems ⋮ Scalable uncertainty quantification for deep operator networks using randomized priors ⋮ DisCoPy: Monoidal Categories in Python ⋮ Extension of dynamic mode decomposition for dynamic systems with incomplete information based on t-model of optimal prediction ⋮ Long-time integration of parametric evolution equations with physics-informed DeepONets ⋮ Unnamed Item ⋮ Optimum design of nonlinear structures via deep neural network-based parameterization framework ⋮ rigidPY: rigidity analysis in Python ⋮ Majorization-minimization-based Levenberg-Marquardt method for constrained nonlinear least squares ⋮ Halting time is predictable for large models: a universality property and average-case analysis ⋮ The hierarchical parity model ⋮ A cornucopia of \(\mathrm{AdS}_5\) vacua ⋮ Unnamed Item ⋮ On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks ⋮ Unnamed Item ⋮ Least-squares solutions of boundary-value problems in hybrid systems ⋮ Moduli-dependent Calabi-Yau and SU(3)-structure metrics from machine learning ⋮ Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks ⋮ A Simple and Efficient Tensor Calculus for Machine Learning ⋮ Imitation learning of stabilizing policies for nonlinear systems ⋮ Wide neural networks of any depth evolve as linear models under gradient descent * ⋮ Multilevel Fine-Tuning: Closing Generalization Gaps in Approximation of Solution Maps under a Limited Budget for Training Data ⋮ Emulation of cardiac mechanics using graph neural networks ⋮ Cryptanalytic extraction of neural network models ⋮ JAX, M.D. A framework for differentiable physics* ⋮ Optimal Energy Shaping via Neural Approximators ⋮ Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation ⋮ Learning stochastic dynamics with statistics-informed neural network
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