Physical Simulations

15 papers with code • 0 benchmarks • 2 datasets

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Greatest papers with code

DiffTaichi: Differentiable Programming for Physical Simulation

taichi-dev/taichi ICLR 2020

We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.

Physical Simulations

UnrealCV: Connecting Computer Vision to Unreal Engine

unrealcv/unrealcv 5 Sep 2016

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.

Physical Simulations

$ξ$-torch: differentiable scientific computing library

xitorch/xitorch 5 Oct 2020

In this work, we present $\xi$-torch, a library of differentiable functionals for scientific simulations.

Physical Simulations

Investigating the relation between chaos and the three body problem

mws262/MAE5730_examples 28 Aug 2020

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

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

PV-Lab/BayesProcess 31 Jan 2020

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 Inference Physical Simulations +1

Bayesian Neural Ordinary Differential Equations

RajDandekar/MSML21_BayesianNODE 14 Dec 2020

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.

Bayesian Inference Machine Learning +2

DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials

ArashRabbani/DeePore 3 May 2020

DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro-tomography images.

Physical Simulations

Can I Pour into It? Robot Imagining Open Containability Affordance of Previously Unseen Objects via Physical Simulations

hongtaowu67/container_imagine 5 Aug 2020

In this letter, we propose a novel method for robots to "imagine" the open containability affordance of a previously unseen object via physical simulations.

Classification General Classification +1