Scalable Computational Methods for Sequential, Science-Based Design of Experiments using Pyomo.DoE

Presentation explaining the new advancements for optimal design of sequential experimental campaign. Scalable computation was achieved through decomposition strategies on models relevant to initiatives across the IDAES software and supporting research portfolio (e.g., PrOMMiS, CCSI2).

Authors

Alexander W. Dowling (ORCID: https://orcid.org/0000-0001-9383-7499)
Daniel J. Laky (ORCID: https://orcid.org/0000-0002-3917-8039)

Institutions

University of Notre Dame (US)

Publication Details

Published: 2025-04-08
DOI: https://doi.org/10.2172/2566797
Primary Topic: Scientific Computing and Data Management
Type: article
Field-Weighted Citation Impact: 0.00

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

Scalable Computational Methods for Sequential, Science-Based Design of Experiments using Pyomo.DoE

Alexander W. Dowling, Daniel J. Laky

Apr 8, 2025

Scientific Computing and Data Management

article

Scalable Computational Methods for Sequential, Science-Based Design of Experiments using Pyomo.DoE

Alexander W. Dowling, Daniel J. Laky

April 8, 2025article en

Abstract

University of Notre Dame (US)

Openalex Percentile: Top 88%

Scientific Computing and Data Management

DOI Open Access PDF OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Scalable Computational Methods for Sequential, Science-Based Design of Experiments using Pyomo.DoE — Alexander W. Dowling, Daniel J. Laky · (2025) | TGRS Research Map | TGRS