Physical Simulations
15 papers with code • 0 benchmarks • 2 datasets
Benchmarks
ABC Dataset
PlasticineLab
Greatest papers with code
DiffTaichi: Differentiable Programming for Physical Simulation
We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.
UnrealCV: Connecting Computer Vision to Unreal Engine
Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which: (i) an agent can perceive, navigate, and take actions guided by AI algorithms, (ii) properties of the worlds can be modified (e. g., material and reflectance), (iii) physical simulations can be performed, and (iv) algorithms can be learnt and evaluated.
$ξ$-torch: differentiable scientific computing library
xitorch/xitorch
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In this work, we present $\xi$-torch, a library of differentiable functionals for scientific simulations.
Investigating the relation between chaos and the three body problem
We review the properties of fractals, the Mandelbrot set and how deterministic chaos ties to the picture.
Physical Simulations
Chaotic Dynamics
Earth and Planetary Astrophysics
Computational Physics
Isometric Transformation Invariant and Equivariant Graph Convolutional Networks
Graphs are one of the most important data structures for representing pairwise relations between objects.
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics
Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum.
Bayesian Neural Ordinary Differential Equations
We demonstrate the successful integration of Neural ODEs with the above Bayesian inference frameworks on classical physical systems, as well as on standard machine learning datasets like MNIST, using GPU acceleration.
Molecular Insights from Conformational Ensembles via Machine Learning
Biomolecular simulations are intrinsically high dimensional and generate noisy data sets of ever-increasing size.
DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials
DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro-tomography images.
Can I Pour into It? Robot Imagining Open Containability Affordance of Previously Unseen Objects via Physical Simulations
In this letter, we propose a novel method for robots to "imagine" the open containability affordance of a previously unseen object via physical simulations.
