A Multifidelity Framework and Uncertainty Quantification for Sea Surface Temperature in the Massachusetts and Cape Cod Bays

Abstract

We present a multifidelity framework to analyze and hindcast predictions of sea surface temperature (SST) in the Massachusetts and Cape Cod Bays, which is a critical area for its ecological significance, sustaining fisheries and the blue economy of the region. Currently, there is a lack of accurate and continuous SST prediction for this region due to the high cost of collecting the samples (e.g., cost of buoys, maintenance, severe weather). In this work, we use SST data from satellite images and in situ measurements collected by the Massachusetts Water Resources Authority to develop multifidelity forecasting models. This multifidelity framework is based on autoregressive Gaussian process schemes that systematically exploit all correlations between data from multiple heterogeneous spatiotemporal sources with various degrees of fidelity. This enables us to obtain implicitly their functional relationships and, at the same time, quantify the uncertainty of the data‐driven predictions. Specifically, in the current work, we develop and validate progressively more complex models, including temporal, spatial, and spatiotemporal multifidelity hindcast predictions of SST in the Massachusetts and Cape Cod Bays. Together with these predictions, we present for the first time uncertainty maps for the region.

Nonlocal Flocking Dynamics: Learning the Fractional Order of PDEs from Particle Simulations

“…It is worth noting that the agent-based model we used in the present work does not consider stochastic terms, and thus the training data of particle trajectories do not contain noise. However, the experimental data of tracking logs obtained by digital imaging may include noise from measurement uncertainty, where a multi-fidelity framework proposed by Babaee et al [32] can be used to handle different sources of uncertainties in the learning process. Moreover, in addition to the GPRbased learning method for connecting individual behavior to collective dynamics, it is also of interest to introduce deep learning strategies such as CNN (convolutional neural network)-based method [33] and the particle swarm optimization algorithm [34] to bridge the gap between flocking theory/modeling and experiments.…”

Fast inverse design of microstructures via generative invariance networks

“…An expected bottleneck of training a surrogate model on high-fidelity labels is the challenge of initial data generation, which may be computationally expensive. Various works in literature have previously explored the idea of leveraging both high-and low-fidelity data to accelerate computational models [29][30][31][32] . We exploit a similar idea by proposing a multi-fidelity surrogate model to circumvent the challenge of generating computationally expensive high-fidelity labels.…”

Fast Inverse Design of Microstructures via Generative Invariance Networks

“…Here, extend the idea to train a regressor that is capable of predicting J sc and FF as continuous values. We train a CNN-based high-fidelity regressor R HF on a dataset of two-phase morphology images with high-fidelity simulated J sc values ranging from 0 to 7 mA/cm 2 low-fidelity data to accelerate computational models [29][30][31][32] . We exploit a similar idea by proposing a multi-fidelity surrogate model to circumvent the challenge of generating computationally expensive high-fidelity labels.…”