Physical Simulations
24 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Physical Simulations
Datasets
ABC Dataset
PlasticineLab
CAMELS Multifield Dataset
ClimART
Most implemented papers
Physics-based Deep Learning
thunil/Physics-Based-Deep-Learning
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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
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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
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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
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
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
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
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
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
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
Biomolecular simulations are intrinsically high dimensional and generate noisy data sets of ever-increasing size.
