Verification, Validation, and Uncertainty Quantification Across Disciplines

Verification, Validation, and Uncertainty Quantification Across Disciplines

May 10-14, 2021


  • Mihai Anitescu (Argonne National Laboratory and Statistics, Chicago)
  • Fausto Cattaneo (Astrophysics, Chicago)
  • Carlo Graziani (Argonne National Laboratory and Astrophysics, Chicago)
  • Robert Rosner (Astrophysics, Chicago)


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.

Confirmed speakers

  • Liliana Borcea (University of Michigan)
  • Donald Estep (Canadian Statistical Sciences Institute, Simon Fraser University)
  • Stephen Eubank (University of Virginia)
  • Roger Ghanem (University of Southern California)
  • Dimitrios Giannakis (New York University)
  • Earl Lawrence (Los Alamos National Laboratory)
  • Ann Lee (Carnegie Mellon University)
  • Andrea Malagoli (SwissRe Corporate Solutions)
  • William Oberkampf (W.L. Oberkampf Consulting)
  • Daniel Sanz-Alonso (University of Chicago)
  • Maike Sonnewald (Princeton University and Geophysical Fluid Dynamics Laboratory)
  • Daniel Tartakovsky (Stanford University)
  • Jonathan Weare (Courant Institute, University of Chicago)
  • David Weisbach (University of Chicago)