We study whether a neural model can replace an explicit physics simulator by learning dynamics directly from visual observations. The framework couples a VAE for image–latent translation with a causal ...

Abstract: In the past decade, there has been growing interest in developing AI-enabled neural interfaces for various neurological disorders and emerging brain-machine interface (BMI) applications. The ...

Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning This repository contains the implementation of neural likelihood estimators for Monte Carlo parameter tuning ...

Physics is the search for and application of rules that can help us understand and predict the world around us. Central to physics are ideas such as energy, mass, particles and waves. Physics attempts ...

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...

Investopedia contributors come from a range of backgrounds, and over 25 years there have been thousands of expert writers and editors who have contributed. Gordon Scott has been an active investor and ...