15 papers with code • 0 benchmarks • 2 datasets
We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.
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 this work, we present $\xi$-torch, a library of differentiable functionals for scientific simulations.
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
Graphs are one of the most important data structures for representing pairwise relations between objects.
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.
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.
DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro-tomography images.
In this letter, we propose a novel method for robots to "imagine" the open containability affordance of a previously unseen object via physical simulations.