Verification, Validation, and Uncertainty Quantification Across Disciplines

Description

With the advent of terascale, petascale and beyond computational capabilities, the reach of computational sciences – both modeling and simulation – is rapidly broadening well beyond its traditional ‘homes’ of physics, chemistry and computational engineering sciences to the biological and social sciences. To the extent to which such modeling and simulation are meant to be predictive in nature – and to the extent to which the systems being simulated are complex in nature – obvious questions regarding the veracity of the computational results must be inevitably confronted. Historically, it is only in the engineering sciences that a formal, comprehensive and rigorous process of verifying and validating (V&V) simulation codes – and defining the error bounds on obtained solutions (e.g., uncertainty quantification, or UQ) – has been developed. In other disciplines, past efforts along such lines have been less systematic and far-reaching, presumably because the consequences of significant errors in the modeling have traditionally not been as consequential as in the engineering disciplines. But it is not only the recent increases in computational capabilities that are changing the situation – it is also the fact that science-based, predictive modeling and simulation are playing an increasing role in supporting political policy decision-making; and in such a context, transparency about the predictive capabilities of such modeling has become increasingly important – the case of global climate change, and the roles played by for example the global climate models and integrated climate impact assessment models, are a key exemplar of this type of interaction between the computational science community and the world at large.

An important element in the development of discipline-appropriate V&V and UQ methods is the extent to which experimentation or, equivalently, data generation allows exploration of the state space of possible solutions. Confidence in the validity of simulations clearly depends on the extent to which simulation results accurately describe the modeled system’s behavior throughout the solution state space; and thus, a naïve expectation would be that experimental constraints on exploring the simulation state space form an obstacle to proper V&V and UQ analyses. Some disciplines are “data-rich”, that is, there are abundant data on the full range of possible experimental outcomes. For others, the data environment is relatively poor, that is, the opportunities for directly validating simulations and for establishing uncertainty bounds are fundamentally limited either in principle or by ethical, legal or practical constraints. In these experimentally constrained instances, one faces fundamental conceptual barriers in the ability to apply the methodologies developed in the data-rich environments. The obvious question is how the highly developed techniques for V&V and UQ in the data-rich (principally engineering) environments can nevertheless make contact with the far more constrained modeling environments defined by disciplines ranging from astrophysics to the social sciences.

The workshop aims to bring together practitioners from across the natural and social sciences, from data rich to data poor environments, together with computer scientists and applied mathematicians involved in developing V&V and UQ methodologies, and to seed interactions between these disparate areas.

Schedule

Videos

What modeling data-rich systems taught me about validating epidemiological models

Stephen Eubank
May 10, 2021

How the Law Addresses Uncertainty

David Weisbach
May 10, 2021

The Risk of Risk in Finance and Insurance

Andrea Malagoli
May 10, 2021

Operator-theoretic approaches for coherent feature extraction in complex systems

Dimitrios Giannakis
May 11, 2021

Simulation-Informed Decision Making

William Oberkampf
May 11, 2021

Learning long-timescale behavior from short trajectory data

Jonathan Weare
May 11, 2021

Calibration and Validation of Approximate Likelihood Models

Ann Lee
May 12, 2021

Graph-based Bayesian Semi-supervised Learning: Prior Design and Posterior Contraction

Daniel Sanz-Alonso
May 12, 2021

Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces

Maike Sonnewald
May 12, 2021

Data driven Reduced Order Modeling for inverse scattering

Liliana Borcea
May 13, 2021

Learning with Uncertainty on Dynamic Manifolds

Daniel Tartakovsky
May 13, 2021

Hierarchy and intrinsic structure for a more credible validation

Roger Ghanem
May 14, 2021