Physical Simulations

24 papers with code • 0 benchmarks • 5 datasets

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Most implemented papers

Physics-based Deep Learning

thunil/Physics-Based-Deep-Learning 11 Sep 2021

This digital book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations.

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.

SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications

MMehdiMousavi/SuperCaustics 23 Jul 2021

In particular, these synthetic datasets omit features such as refraction, dispersion and caustics due to limitations in the rendering pipeline.

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.

Physics-driven Fire Modeling from Multi-view Images

Garoe/bath-fire-shader 14 Apr 2018

This allows for a number of novel phenomena such as global fire illumination effects.

Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body

hongtaowu67/container_imagine 17 Sep 2019

In our method, the robot "imagines" the affordance of an arbitrarily oriented object as a chair by simulating a physical sitting interaction between an articulated human body and the object.

3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries

ctlee/gamer 17 Dec 2019

An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries.

Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders

mgolbabaee/LRTV-MRFResnet-for-MRFingerprinting 23 Jan 2020

We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing.

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.

Molecular Insights from Conformational Ensembles via Machine Learning

delemottelab/demystifying Biophys Journal 2020

Biomolecular simulations are intrinsically high dimensional and generate noisy data sets of ever-increasing size.