Total Energy
36 papers with code • 0 benchmarks • 1 datasets
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MD17
Most implemented papers
Deep learning of thermodynamics-aware reduced-order models from data
quercushernandez/DeepLearningMOR
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We present an algorithm to learn the relevant latent variables of a large-scale discretized physical system and predict its time evolution using thermodynamically-consistent deep neural networks.
Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints
We consider the problem of constrained multi-objective blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions satisfying a set of constraints while minimizing the number of function evaluations.
Encoded Prior Sliced Wasserstein AutoEncoder for learning latent manifold representations
chimeraki/EPSWAE
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While variational autoencoders have been successful in several tasks, the use of conventional priors are limited in their ability to encode the underlying structure of input data.
Data-Driven Copy-Paste Imputation for Energy Time Series
The CPI method copies data blocks with similar properties and pastes them into gaps of the time series while preserving the total energy of each gap.
Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control
This paper proposes a Hamiltonian formulation over the SE(3) manifold of the structure of a neural ordinary differential equation (ODE) network to approximate the dynamics of a rigid body.
Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
Electron density $\rho(\vec{r})$ is the fundamental variable in the calculation of ground state energy with density functional theory (DFT).
Optimal activity and battery scheduling algorithm using load and solar generation forecast
ryuan/ieee-cis-data-challenge-fresno
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In this report, we provide a technical sequence on tackling the solar PV and demand forecast as well as optimal scheduling problem proposed by the IEEE-CIS 3rd technical challenge on predict + optimize for activity and battery scheduling.
Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise
Additionally, the online adaptive process achieves an F1 score of 99% across five different exercise intensities, with a total energy consumption of 1. 55+-0. 54~mJ.
SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks
Based on SATA, we show quantitative analyses of the energy efficiency of SNN training and compare the training cost of SNNs and ANNs.
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials.
