IEEE

Efficient Electromagnetic Near-Field Scanning Using Physics-Informed Gaussian Process Regression

This paper proposes a novel approach combining prior physics-based Gaussian Process Regression (GPR) with Bayesian Optimization for efficient and accurate electromagnetic near-field scanning. By ...
IEEE

Incremental Heteroscedastic Gaussian Process Regression and Its Applications in Model Predictive Control

Abstract: Gaussian process regression (GPR) models are becoming increasingly tightly integrated into robotic systems, particularly in the context of robot model predictive control (MPC) operating in ...
GitHub

Gap-averaged flow simulations with Gaussian Process regression.

The published wheels are currently not built with LAMMPS. Thus, running multiscale simulations with molecular dynamics is not possible with this quick installation. For the full functionality it is ...
Interesting Engineering

New adaptive system lets robots replicate human touch with far less training data

Researchers in Japan have developed an adaptive motion reproduction system that allows robots to generate human-like movements using surprisingly small amounts of training data. Despite rapid advances ...
Hosted on MSN

Build logistic regression in Python from scratch easily

Implement Logistic Regression in Python from Scratch ! In this video, we will implement Logistic Regression in Python from Scratch. We will not use any build in models, but we will understand the code ...
eLife

Learning sequence-function relationships with scalable, interpretable Gaussian processes

This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...
GitHub

A minimal, portable Gaussian Splatting sandbox for rapid small-scale experimentation and performance insights in pure Python.

Gaussian Splatting is a cutting-edge 3D representation technique that models a scene as a set of learnable 3D Gaussian primitives. Each Gaussian defines a point in space with position, color, opacity, ...
INSPIRE

Gaussian process analysis of type-B quasiperiodic oscillations in the black hole X-ray binary MAXI J1348–630

We analyzed Insight-HXMT data of the black hole X-ray binary MAXI J1348–630 during the type-B quasiperiodic oscillation (QPO) phase of its 2019 outburst. Using the Gaussian process method, we applied ...
Scientific Research Publishing

Rasmussen, C.E. and Williams, C.K.I. (2005) Gaussian Processes for Machine Learning. The MIT Press.

ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...
C&EN

Data-Driven Design of High-Temperature-Resistant Polyimides Using Hierarchical Gaussian Process Regression

School of Materials Science and Engineering, Beihang University, Beijing 100191, China State Key Laboratory of Artificial Intelligence for Materials Science, Beihang University, Beijing 100091, China ...
C&EN

Efficient Implementation of Gaussian Process Regression Accelerated Saddle Point Searches with Application to Molecular Reactions

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...