24 papers with code • 0 benchmarks • 5 datasets
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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
In particular, these synthetic datasets omit features such as refraction, dispersion and caustics due to limitations in the rendering pipeline.
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.
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.
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.