Physical Simulations
38 papers with code • 0 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Physical Simulations
Datasets
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ABC Dataset
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PlasticineLab
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CAMELS Multifield Dataset
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ClimART
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Expressive Gaussian mixture models for high-dimensional statistical modelling: simulated data and neural network model files
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A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances
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2D_NACA_RANS
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Workshop Tools Dataset
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DrivAerNet
Latest papers with no code
C(NN)FD -- a deep learning framework for turbomachinery CFD analysis
Deep Learning methods have seen a wide range of successful applications across different industries.
A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction
To overcome these challenges, we propose the crystal-specific pre-training framework for learning crystal representations with self-supervision.
A high-efficiency model indicating the role of inhibition in the resilience of neuronal networks to damage resulting from traumatic injury
The computational cost of these simulations complicates the investigation of the effects of such damage at a network level.
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods
Recent developments in Machine Learning approaches for modelling physical systems have begun to mirror the past development of numerical methods in the computational sciences.
TPA-Net: Generate A Dataset for Text to Physics-based Animation
Recent breakthroughs in Vision-Language (V&L) joint research have achieved remarkable results in various text-driven tasks.
ClimateNeRF: Extreme Weather Synthesis in Neural Radiance Field
Physical simulations produce excellent predictions of weather effects.
Exploring Physical Latent Spaces for High-Resolution Flow Restoration
We explore training deep neural network models in conjunction with physics simulations via partial differential equations (PDEs), using the simulated degrees of freedom as latent space for a neural network.
Wavelet-based Loss for High-frequency Interface Dynamics
As an alternative, we present a new method based on a wavelet loss formulation, which remains transparent in terms of what is optimized.
Neural Posterior Estimation with Differentiable Simulators
Simulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions.
φ-SfT: Shape-from-Template with a Physics-Based Deformation Model
In contrast to previous works, this paper proposes a new SfT approach explaining 2D observations through physical simulations accounting for forces and material properties.
