Data Science for Computational Modeling (DSCM): Design, Uncertainty Quantification, and Optimization
DETAILS
Call for Papers
Data Science for Computational Modeling (DSCM): Design, Uncertainty Quantification, and Optimization
Journal: IISE Transactions
Publisher: Taylor & Francis
Submission deadline: 31 July 2026
This special issue focuses on data-driven computational modeling methods that make high-fidelity simulations faster, smarter, and more reliable. It brings together research on experimental design, surrogate modeling, uncertainty quantification, physics-informed machine learning, and optimization under uncertainty across science and engineering applications.
The call highlights the growing role of data-centric methods in areas such as aircraft design, climate forecasting, advanced manufacturing, precision medicine, digital twins, and resilient energy systems. It also emphasizes challenges such as multifidelity data fusion, model-form error, scalable algorithms, and translating methods into robust industrial software.
Special Issue Editors
Professor Rui Tuo, Texas A&M University.
Professor Xinwei Deng, Virginia Tech.
Professor Lulu Kang, University of Massachusetts.
Professor Xiao Liu, Georgia Institute of Technology.
Professor Ozge Surer, Miami University.
Scope
The special issue welcomes work on experimental design for computer experiments, surrogate modeling, sensitivity analysis, verification and validation, physics-informed and hybrid machine learning, optimization under uncertainty, scalable implementations, statistical learning for reduced-order modeling, multimodal modeling, risk-aware decision-making, and case studies on complex systems.
Submission details
Manuscripts should be submitted through the IISE Transactions submission system and categorized as Special Issue. The submission deadline is 31 July 2026, with review milestones and publication planned through mid-2027.
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