Physical Simulations
38 papers with code • 0 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Physical Simulations
Datasets
-
ABC Dataset
-
PlasticineLab
-
CAMELS Multifield Dataset
-
ClimART
-
Expressive Gaussian mixture models for high-dimensional statistical modelling: simulated data and neural network model files
-
A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances
-
2D_NACA_RANS
-
Workshop Tools Dataset
-
DrivAerNet
Latest papers
Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics
tum-pbs/sfbc
•
•
Learning physical simulations has been an essential and central aspect of many recent research efforts in machine learning, particularly for Navier-Stokes-based fluid mechanics.
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations
llnl/gplasdi
•
•
Traditional partial differential equation (PDE) solvers can be computationally expensive, which motivates the development of faster methods, such as reduced-order-models (ROMs).
Neural General Circulation Models for Weather and Climate
Here we present the first GCM that combines a differentiable solver for atmospheric dynamics with ML components, and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best ML and physics-based methods.
Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus
Further, we show that DEC allows to implement a strongly-typed SR procedure that guarantees the mathematical consistency of the recovered models and reduces the search space of symbolic expressions.
Climate-sensitive Urban Planning through Optimization of Tree Placements
lmb-freiburg/tree-planting
•
•
We show the efficacy of our approach across a wide spectrum of study areas and time scales.
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training
Our extensive experiments show that DeepZero achieves state-of-the-art (SOTA) accuracy on ResNet-20 trained on CIFAR-10, approaching FO training performance for the first time.
Multi-Resolution Active Learning of Fourier Neural Operators
shib0li/mra-fno
•
•
To overcome this problem, we propose Multi-Resolution Active learning of FNO (MRA-FNO), which can dynamically select the input functions and resolutions to lower the data cost as much as possible while optimizing the learning efficiency.
Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting
Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design.
Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark
junchengli1/Sim-Suction-API
•
•
This paper presents Sim-Suction, a robust object-aware suction grasp policy for mobile manipulation platforms with dynamic camera viewpoints, designed to pick up unknown objects from cluttered environments.
Grounding Graph Network Simulators using Physical Sensor Observations
jlinki/ggns
•
•
Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail.
