Accurate and efficient surrogate modeling is essential for modern computational science, and there are a staggering number of emulation methods to choose from. With new methods being developed all the time, comparing the relative strengths and weaknesses of different methods remains a challenge due to inconsistent benchmarking practices and (sometimes) limited reproducibility and transparency. In this work, we present a large-scale, fully reproducible comparison of $29$ distinct emulators across $60$ canonical test functions and $40$ real emulation datasets. To facilitate rigorous, apples-to-apples comparisons, we introduce the R package \texttt{duqling}, which streamlines reproducible simulation studies using a consistent, simple syntax, and automatic internal scaling of inputs. This framework allows researchers to compare emulators in a unified environment and makes it possible to replicate or extend previous studies with minimal effort, even across different publications. Our results provide detailed empirical insight into the strengths and weaknesses of state-of-the-art emulators and offer guidance for both method developers and practitioners selecting a surrogate for new data. We discuss best practices for emulator comparison and highlight how \texttt{duqling} can accelerate research in emulator design and application.

The computational model can be assessed through comparison of these predictions to test data generated from an experiment.

In an increasing number of applications designers have access to multiple computer models which typically have different levels of fidelity and cost. Traditionally, designers calibrate these models one at a time against some high-fidelity data (e.g., experiments). In this paper, we question this tradition and assess the potential of calibrating multiple computer models at the same time. To this end, we develop a probabilistic framework that is founded on customized neural networks (NNs) that are designed to calibrate an arbitrary number of computer models. In our approach, we (1) consider the fact that most computer models are multi-response and that the number and nature of calibration parameters may change across the models, and (2) learn a unique probability distribution for each calibration parameter of each computer model, (3) develop a loss function that enables our NN to emulate all data sources while calibrating the computer models, and (4) aim to learn a visualizable latent space where model-form errors can be identified. We test the performance of our approach on analytic and engineering problems to understand the potential advantages and pitfalls in simultaneous calibration of multiple computer models. Our method can improve predictive accuracy, however, it is prone to non-identifiability issues in higher-dimensional input spaces that are normally constrained by underlying physics.

In this study, we focus on developing efficient calibration methods via Bayesian decision-making for the family of compartmental epidemiological models. The existing calibration methods usually assume that the compartmental model is cheap in terms of its output and gradient evaluation, which may not hold in practice when extending them to more general settings. Therefore, we introduce model calibration methods based on a "graybox" Bayesian optimization (BO) scheme, more efficient calibration for general epidemiological models. This approach uses Gaussian processes as a surrogate to the expensive model, and leverages the functional structure of the compartmental model to enhance calibration performance. Additionally, we develop model calibration methods via a decoupled decision-making strategy for BO, which further exploits the decomposable nature of the functional structure. The calibration efficiencies of the multiple proposed schemes are evaluated based on various data generated by a compartmental model mimicking real-world epidemic processes, and real-world COVID-19 datasets. Experimental results demonstrate that our proposed graybox variants of BO schemes can efficiently calibrate computationally expensive models and further improve the calibration performance measured by the logarithm of mean square errors and achieve faster performance convergence in terms of BO iterations. We anticipate that the proposed calibration methods can be extended to enable fast calibration of more complex epidemiological models, such as the agent-based models.

This paper studies the numerical computation of integrals, representing estimates or predictions, over the output f(x) of a computational model with respect to a distribution p(dx) over uncertain inputs x to the model. For the functional cardiac models that motivate this work, neither f nor p possess a closed-form expression and evaluation of either requires 100 CPU hours, precluding standard numerical integration methods. Our proposal is to treat integration as an estimation problem, with a joint model for both the a priori unknown function f and the a priori unknown distribution p. The result is a posterior distribution over the integral that explicitly accounts for dual sources of numerical approximation error due to a severely limited computational budget. This construction is applied to account, in a statistically principled manner, for the impact of numerical errors that (at present) are confounding factors in functional cardiac model assessment.

The Environmental Protection Agency has scrapped a plan to phase out mammal testing for studying chemical toxicity, Science reports. In 2019, the regulatory agency vowed to completely phase out animal testing for toxicology studies by 2035 in favor of non-animal "test subjects" programmed into computer models. The call to challenge the status quo was controversial from the start -- it not only was going to impact thousands of studies and experiments, but many scientists argued that computer models were nowhere near ready to replace animals as test subjects. In a letter written by a group of public health officials, the experts urged the EPA's head Michael Regan to reconsider the ban because computational models, in their opinion, were "not yet developed to the point" where they could be relied on for risk assessments. In order for the new ban to have taken effect, the EPA said there needed to be "scientific confidence" that non-animal models could soundly replace critters like mice and rabbits in labs.

Pulmonary Hypertension (PH) is a severe disease characterized by an elevated pulmonary artery pressure. The gold standard for PH diagnosis is measurement of mean Pulmonary Artery Pressure (mPAP) during an invasive Right Heart Catheterization. In this paper, we investigate noninvasive approach to PH detection utilizing Magnetic Resonance Imaging, Computer Models and Machine Learning. We show using the ablation study, that physics-informed feature engineering based on models of blood circulation increases the performance of Gradient Boosting Decision Trees-based algorithms for classification of PH and regression of values of mPAP. We compare results of regression (with thresholding of estimated mPAP) and classification and demonstrate that metrics achieved in both experiments are comparable. The predicted mPAP values are more informative to the physicians than the probability of PH returned by classification models. They provide the intuitive explanation of the outcome of the machine learning model (clinicians are accustomed to the mPAP metric, contrary to the PH probability).

You know the smell of warm, buttered popcorn. The pungent, somewhat sweet scent that precedes rain. But could you begin to describe these aromas in detail? Your nose has some 400 olfactory receptors that do the work of translating the world's estimated 40 billion odorous molecules into an even higher number of distinct scents your brain can understand. Yet although children are taught that grass is green and pigmented by chlorophyll, they rarely learn to describe the smell of a freshly cut lawn, let alone the ozone before a storm.

A new model has revealed that a mega volcano eruption drove the dinosaurs to extinction -- not the infamous Chicxulub meteor that smashed into the Yucatán Peninsula over 66 million years ago. Scientists from Dartmouth University designed a simulation that used real-world geological data to crunch more than 300,000 possible scenarios. The system was prompted to explain the fossil records across the one million years before and after dinosaurs became extinct. The model revealed that climate change and toxic gases from the Deccan Traps' hundreds of thousands of years of emissions were the nail in the coffin for the extinct creatures. India's'Deccan Traps' mega-volcano, estimated to have pumped as much as 10.4 trillion tons of carbon dioxide and 9.3 trillion tons of sulfur dioxide into Earth's atmosphere during their nearly million years of eruptions.

When Aomawa Shields temporarily left astronomy in the 1990s for a life in the theater, no one knew whether planets existed beyond our solar system. By the time she returned to academia 11 years later, hundreds of exoplanets had been discovered. Today, telescopes and detection methods have advanced so much that the discoveries number close to 6,000. Shields, now an astrobiologist at UC Irvine, studies these distant worlds using computer models to evaluate their climates and assess whether they might be friendly to alien life. During this second stint in academia, she completed her PhD at age 39 and afterward gave birth to her daughter.