Design of a complex system involves a rich set of information sources, including such diverse sources as physics-based models, experiments, historical data, and expert opinions. There is an increasing recognition of the value of mathematical methods that provide systematic guidance in managing these information sources in support of decisions across the design phase.

What next? Sequentially value-optimal engineering tasking for analysis and design

Douglas Allaire, Assistant Professor, TAMU

Data-driven design using digital thread

Victor Singh, PhD student, MIT

A surrogate-based optimization framework for the deployment of stormwater infrastructures in large watersheds

Ng Jia Yi, PhD student, SUTD

Model reduction has in recent years become a widespread approach to mitigate the computational cost of solving large-scale systems such as those resulting from discretization of partial differential equations. Recent developments in combining traditional projection-based model reduction methods with machine learning methods have led to new approaches that tune and adapt reduced models in the face of changing system properties and dynamic data. This provides new opportunities to discover and learn models, informed by physics-based first principles but guided by data.

Model reduction has in recent years become a widespread approach to mitigate the computational cost of solving large-scale systems such as those resulting from discretization of partial differential equations. Considerable academic advances have been made in model reduction methods, yet impact in industry has been limited. Several key challenges must be addressed to make model reduction applicable – and applied – to a broader range of problems. First, research is needed to address the challenges of nonlinear systems; second, methods are needed that overcome the intrusive nature of existing model reduction approaches yet retain the rigor of the approaches.

Model order reduction for continuous chemical processing and control

Nguyen Van Bo, A*STAR

Lifting nonlinear systems: More structure, more opportunities for ROM?

Boris Kramer, Postdoc, MIT

Challenges in data-driven reduced order modeling for combustion problems

Elizabeth Qian, PhD student, MIT

An inverse problem seeks to estimate an unknown and unobservable parameter from (often indirect and noisy) measurements. Inverse problems are pervasive in science and engineering, yet solving them at scale for systems governed by complex nonlinear dynamics is a significant challenge. This challenge can be addressed through approaches that leverage low-dimensional approximations that exploit system structure.

Subspace acceleration for large-scale Bayesian inverse problems

Tiangang Cui, Lecturer, Monash

Nonlinear goal-oriented inference

Harriet Li, PhD student, MIT

The many sources of uncertainty in the early stages of design pose critical risks to program success. Uncertainty arises due to the uncertain models used to make critical decisions in the design process, programmatic uncertainties in cost and schedule, the uncertainty surrounding advanced technologies that are often employed to push the envelope in system performance, the uncertain manufacturing processes that result in as-built systems that may deviate from as-designed ones, and the uncertain external environment in which these systems must operate.

Data-enhanced modeling for aircraft design and air transportation

Rhea Liem, Assistant Professor, HKUST

Optimization under uncertainty using multiple dominance criteria for aerospace design

Laurence Cook, Postdoc, MIT

Multi-fidelity methodologies for conceptual design under uncertainty

Alex Feldstein, SM student, MIT