Optimal experimental design: Formulations and computations
Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …
natural and social sciences, engineering applications, and beyond. Optimal experimental …
Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials
This paper provides a coherent and efficient computational framework for stochastic
multiscale analysis of material systems in the presence of parametric uncertainties and …
multiscale analysis of material systems in the presence of parametric uncertainties and …
An extended polynomial chaos expansion for PDF characterization and variation with aleatory and epistemic uncertainties
This paper presents an extended polynomial chaos formalism for epistemic uncertainties
and a new framework for evaluating sensitivities and variations of output probability density …
and a new framework for evaluating sensitivities and variations of output probability density …
Bayesian model updating with finite element vs surrogate models: Application to a miter gate structural system
Bayesian finite element (FE) model updating using direct model evaluations of large-scale
high-fidelity FE models is extremely computationally expensive. Surrogate models can be …
high-fidelity FE models is extremely computationally expensive. Surrogate models can be …
Breaking down the computational barriers to real‐time urban flood forecasting
Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there
are no operational systems to forecast flooding at spatial resolutions that can facilitate …
are no operational systems to forecast flooding at spatial resolutions that can facilitate …
Model identification in reactor-based combustion closures using sparse symbolic regression
Abstract In Large Eddy Simulations (LES) of combustion, the accuracy of predictions might
be heavily affected by deficiencies in traditional/simplified closure models, especially when …
be heavily affected by deficiencies in traditional/simplified closure models, especially when …
Multiscale modeling of compartmentalized reservoirs using a hybrid clustering-based non-local approach
Representing the reservoir as a network of discrete compartments with neighbor and non-
neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs …
neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs …
Stochastic modeling and statistical calibration with model error and scarce data
This paper introduces a procedure to assess the predictive accuracy of stochastic models
subject to model error and sparse data. Model error is introduced as uncertainty on the …
subject to model error and sparse data. Model error is introduced as uncertainty on the …
Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models
Applications of process‐based models (PBM) for predictions are confounded by multiple
uncertainties and computational burdens, resulting in appreciable errors. A novel modeling …
uncertainties and computational burdens, resulting in appreciable errors. A novel modeling …
Towards robust statistical inference for complex computer models
J Oberpriller, DR Cameron, MC Dietze… - Ecology Letters, 2021 - Wiley Online Library
Ecologists increasingly rely on complex computer simulations to forecast ecological
systems. To make such forecasts precise, uncertainties in model parameters and structure …
systems. To make such forecasts precise, uncertainties in model parameters and structure …