The following pages link to torchdiffeq (Q46791):
Displaying 42 items.
- PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network (Q2222627) (← links)
- Generalization of partitioned Runge-Kutta methods for adjoint systems (Q2223878) (← links)
- Collocation based training of neural ordinary differential equations (Q2236696) (← links)
- Spatiotemporal adaptive neural network for long-term forecasting of financial time series (Q2237157) (← links)
- Data-driven reduced bond graph for nonlinear multiphysics dynamic systems (Q2244143) (← links)
- Deep learning as optimal control problems: models and numerical methods (Q2297872) (← links)
- Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective (Q2302458) (← links)
- A mean-field optimal control formulation of deep learning (Q2319864) (← links)
- Deep learning of biological models from data: applications to ODE models (Q2659803) (← links)
- Towards fast weak adversarial training to solve high dimensional parabolic partial differential equations using XNODE-WAN (Q2671351) (← links)
- A hybrid objective function for robustness of artificial neural networks -- estimation of parameters in a mechanical system (Q2672200) (← links)
- Deep Variational Inference (Q3300544) (← links)
- (Q4969121) (← links)
- Neural ODEs as the deep limit of ResNets with constant weights (Q4995042) (← links)
- (Q4998938) (← links)
- (Q4998956) (← links)
- Stabilizing Invertible Neural Networks Using Mixture Models (Q5002576) (← links)
- (Q5011561) (← links)
- Artificial Neural Networks for the Estimation of Pedestrian Interaction Forces (Q5012168) (← links)
- Structure-preserving deep learning (Q5014474) (← links)
- Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements (Q5014838) (← links)
- Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
- Augmenting physical models with deep networks for complex dynamics forecasting* (Q5020055) (← links)
- Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models (Q5022489) (← links)
- A Nonautonomous Equation Discovery Method for Time Signal Classification (Q5024512) (← links)
- Layer-Parallel Training of Deep Residual Neural Networks (Q5027015) (← links)
- EnResNet: ResNets Ensemble via the Feynman--Kac Formalism for Adversarial Defense and Beyond (Q5037566) (← links)
- Deep Neural Networks, Generic Universal Interpolation, and Controlled ODEs (Q5037577) (← links)
- A Neural Network Approach to Sampling Based Learning Control for Quantum System with Uncertainty (Q5065154) (← links)
- VAE-KRnet and Its Applications to Variational Bayes (Q5077693) (← links)
- A Symplectic Based Neural Network Algorithm for Quantum Controls under Uncertainty (Q5077710) (← links)
- (Q5094126) (← links)
- slimTrain---A Stochastic Approximation Method for Training Separable Deep Neural Networks (Q5095499) (← links)
- Learning latent dynamics for partially observed chaotic systems (Q5139802) (← links)
- Discovery of Dynamics Using Linear Multistep Methods (Q5151929) (← links)
- EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models (Q5154728) (← links)
- (Q5159460) (← links)
- Implicit Deep Learning (Q5162622) (← links)
- Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection (Q5162626) (← links)
- Neuro-optimized numerical treatment of HIV infection model (Q5164567) (← links)
- Mining gold from implicit models to improve likelihood-free inference (Q5854829) (← links)
- Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks (Q5860356) (← links)
