Results 61 to 70 of about 461,664 (195)

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

, 2019
Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters
Chou, Ching-Shan, Hazarika, Subhashis, Li, Haoyu, Shen, Han-Wei, Wang, Ko-Chih   +4 more
core   +1 more source

Uncertainty quantification of surrogate models using conformal prediction

Machine Learning: Science and Technology
Data-driven surrogate models offer fast, inexpensive approximations to complex numerical and experimental systems but typically lack uncertainty quantification, limiting their reliability in safety-critical applications.
Vignesh Gopakumar, Ander Gray, Joel Oskarsson, Lorenzo Zanisi, Daniel Giles, Matt J Kusner, Stanislas Pamela, Marc Peter Deisenroth   +7 more
doaj   +1 more source

Multi-Objective Optimization of Building Energy Consumption: A Case Study of Temporary Buildings on Construction Sites

Buildings
Building energy consumption management significantly impacts energy efficiency, environmental effects, and economic benefits throughout a building’s life cycle.
Xiaohui Guo, Yuanfeng Wang, Yinshan Liu, Lei Fan, Shaoqin Xue, Chengcheng Shi, Lei Pan, Boqun Zhang, Liping Wang, Xinlei Chang   +9 more
doaj   +1 more source

On Design Mining: Coevolution and Surrogate Models [PDF]

Artificial Life, 2017
Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation.
Richard John Preen, Larry Bull
openaire   +3 more sources

Convergence of Weak-SINDy Surrogate Models

SIAM Journal on Applied Dynamical Systems
In this paper, we give an in-depth error analysis for surrogate models generated by a variant of the Sparse Identification of Nonlinear Dynamics (SINDy) method. We start with an overview of a variety of non-linear system identification techniques, namely, SINDy, weak-SINDy, and the occupation kernel method.
Benjamin P. Russo, M. Paul Laiu
openaire   +3 more sources

A Python surrogate modeling framework with derivatives [PDF]

Advances in Engineering Software, 2019
Abstract The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods.
Mohamed Amine Bouhlel, John T. Hwang, Nathalie Bartoli, Rémi Lafage, Joseph Morlier, Joaquim R. R. A. Martins   +5 more
openaire   +4 more sources

Point-Wise Full-Field Physics Neural Mapping Framework via Boundary Geometry Constrained for Large Thermoplastic Deformation

Algorithms
Computation modeling for large thermoplastic deformation of plastic solids is critical for industrial applications like non-invasive assessment of engineering components.
Jue Wang, Xinyi Xu, Changxin Ye, Wei Huangfu   +3 more
doaj   +1 more source

mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Location

, 2020
This paper develops mfEGRA, a multifidelity active learning method using data-driven adaptively refined surrogates for failure boundary location in reliability analysis.
Chaudhuri, Anirban, Marques, Alexandre N., Willcox, Karen E.   +2 more
core   +1 more source

Full Domain Analysis in Fluid Dynamics

Machine Learning and Knowledge Extraction
Novel techniques in evolutionary optimization, simulation, and machine learning enable a broad analysis of domains like fluid dynamics, in which computation is expensive and flow behavior is complex.
Alexander Hagg, Adam Gaier, Dominik Wilde, Alexander Asteroth, Holger Foysi, Dirk Reith   +5 more
doaj   +1 more source

A Derivative-Free Trust-Region Algorithm for Reliability-Based Optimization

, 2016
In this note, we present a derivative-free trust-region (TR) algorithm for reliability based optimization (RBO) problems. The proposed algorithm consists of solving a set of subproblems, in which simple surrogate models of the reliability constraints are
Gao, Tian, Li, Jinglai
core   +1 more source