Four methods for generation of turbulent phase screens: comparison. (arXiv:1911.09185v1 [eess.IV])

We introduce a new method for generation of the phase screen samples with arbitrary spatial spectrum: Sparse Spectrum with uniform wave vectors (SU). Similar to the known Sparse Spectrum (SS) technique, it uses trigonometric series with random discrete support on the wave vector plane, but, unlike the SS technique, the random wave vectors are uniformly…

MPRAD: A Monte Carlo and ray-tracing code for the proton radiography in high-energy-density plasma experiments. (arXiv:1911.09192v1 [physics.plasm-ph])

Proton radiography is used in various high-energy-density (HED) plasma experiments. In this paper, we describe a Monte Carlo and ray-tracing simulation tool called MPRAD that can be used for modeling the deflection of proton beams in arbitrary three dimensional electromagnetic fields, as well as the diffusion of the proton beams by Coulomb scattering and stopping…

Iterative Peptide Modeling With Active Learning And Meta-Learning. (arXiv:1911.09103v1 [q-bio.BM])

Often the development of novel materials is not amenable to high-throughput or purely computational screening methods. Instead, materials must be synthesized one at a time in a process that does not generate significant amounts of data. One way this method can be improved is by ensuring that each experiment provides the best improvement in both…

Reversible Computation in Wireless Communications. (arXiv:1911.09104v1 [cs.ET])

This chapter presents the pioneering work in applying reversible computation paradigms to wireless communications. These applications range from developing reversible hardware architectures for underwater acoustic communications to novel distributed optimisation procedures in large radio-frequency antenna arrays based on reversing Petri nets. Throughout the chapter, we discuss the rationale for introducing reversible computation in the domain…

On Universal Features for High-Dimensional Learning and Inference. (arXiv:1911.09105v1 [cs.LG])

We consider the problem of identifying universal low-dimensional features from high-dimensional data for inference tasks in settings involving learning. For such problems, we introduce natural notions of universality and we show a local equivalence among them. Our analysis is naturally expressed via information geometry, and represents a conceptually and computationally useful analysis. The development reveals…

Automatic Differentiable Monte Carlo: Theory and Application. (arXiv:1911.09117v1 [physics.comp-ph])

Differentiable programming has emerged as a key programming paradigm empowering rapid developments of deep learning while its applications to important computational methods such as Monte Carlo remain largely unexplored. Here we present the general theory enabling infinite-order automatic differentiation on expectations computed by Monte Carlo with unnormalized probability distributions, which we call “automatic differentiable Monte…

A Scrambled Method of Moments. (arXiv:1911.09128v1 [econ.EM])

Quasi-Monte Carlo (qMC) methods are a powerful alternative to classical Monte-Carlo (MC) integration. Under certain conditions, they can approximate the desired integral at a faster rate than the usual Central Limit Theorem, resulting in more accurate estimates. This paper explores these methods in a simulation-based estimation setting with an emphasis on the scramble of Owen…

DPM: A deep learning PDE augmentation method (with application to large-eddy simulation). (arXiv:1911.09145v1 [cs.LG])

Machine learning for scientific applications faces the challenge of limited data. We propose a framework that leverages a priori known physics to reduce overfitting when training on relatively small datasets. A deep neural network is embedded in a partial differential equation (PDE) that expresses the known physics and learns to describe the corresponding unknown or…