MS48 48.Advances in Strategies for Uncertainty Quantification and Robust/Performance-based Design of Structures and Systems Exposed to Natural and Man-made Hazards
Assistant Prof. Seymour M.J. Spence: email@example.com
Seymour M.J. Spence, Assistant Professor, Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA, firstname.lastname@example.org
Alexandros Taflanidis, Associate Professor, Department of Civil & Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN, USA, email@example.com
Michael D. Shields, Assistant Professor, Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA, firstname.lastname@example.org
Abstract of the special session:
The modeling and prediction of the effects of natural and man-made hazards on structures/systems, whether civil, mechanical, navel or aerospace, is one of the core challenges of engineering. Over the past decades, numerous methodologies have been developed/formulated to this end, incorporating reliability-/risk-/resiliency-/performance-based concepts. While these approaches may appear diverse, they all have in common the need for efficient propagation of uncertainty through computational models (i.e., finite element models) of complex structures and systems. These models are inevitably characterized by nonlinear behaviors, heterogeneous design parameters and high dimensionality, therefore defining complex computational environments. Recent advances in methods such as surrogate/metamodeling approaches, reduced-order modeling, and machine learning, are leading to unprecedented possibilities in predicting, designing, operating, and monitoring engineering or environmental systems modeled in these complex computational environments.
The aim of this mini-symposium is to provide an opportunity for researchers in the fields of reduced-order modeling, surrogate/metamodeling approaches, multi-fidelity simulation, Bayesian inference, numerical methods for large-scale optimization, machine learning, and uncertainty quantification to present their current research efforts as well as future directions. Contributions addressing theoretical and computational developments, numerical algorithms and practical applications from different sub-fields of engineering where uncertainty and complex computational models occur (e.g. risk management and optimization, modeling of hazards and extreme events, stochastic dynamics, robust optimization, reliability-/risk-/resiliency-/performance-based design, applications involving Computational Fluid Dynamics (CFD) simulations) are welcome. The mini-symposium will provide an opportunity to bring together researchers, academics, and practicing engineers active in these topical areas to share their experience and latest research results.
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