To evaluate ∇θLθphysics and ∇θLθdata we use torchdiffeq36 –a pytorch-compatible implementation of the Neural ODE framework. To the best of our knowledge, this is the first framework that combines non-l
We are using torchdiffeq as the Neural ODEs library. We thank the author for sharing their codes.AboutContinuous Exposure Learning for Low-light Image Enhancement using Neural ODEs, in ICLR 2025 (Spotlight) Topicsneuralode llie ResourcesReadme ...
To evaluate ∇θLθphysics and ∇θLθdata we use torchdiffeq36 –a pytorch-compatible implementation of the Neural ODE framework. To the best of our knowledge, this is the first framework that combines non-linear latent-dynamics (Neural ODE), autoencoders, and a physics-informed loss ...
For convenience, we have implemented an FCNN –fully-connected neural network, whose hidden units and activation functions can be customized. from neurodiffeq.networks import FCNN # Default: n_input_units=1, n_output_units=1, hidden_units=[32, 32], activation=torch.nn.Tanh net1 = FCNN(...
Due to the potential for airborne radar to capture incomplete observational information regarding unmanned aerial vehicle (UAV) trajectories, this study introduces a novel approach called Node-former, which integrates neural ordinary differential equations (NODEs) and the Informer framework. The proposed me...
As an example, we applied the Graph-AE model to quickly build an ANI-1 type neural network potential energy surface (NNPES) for the Ace-ALA-Nme peptide using the default setting of the Torch-ANI package [36]. First, 6000 conformations were extracted from a 60 ns MD simulation trajectory...