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## Deep Learning-Based

Multimodal Data Fusion

Our project aims to develop advanced methods that will

synergistical exploit multiple, complementing, heterogenous

data sources to estimate true states of large dynamical systems.

We are developing deep learning-based data fusion framework

that uses trained differentiable surrogates within a Bayesian

inference optimization problem to quantify the posterior.

The primary benefit of the data-fusion paradigm is to rapidly

adapt pre-trained data-driven models to real-time, potentially

sparse, observations providing increased robustness in

nowcasting ability with quantified uncertainty.

## Deep Learning-Based

Multimodal Data Fusion

Our project aims to develop advanced methods that will synergistical exploit multiple, complementing, heterogenous data sources to estimate true states of large dynamical systems. We are developing deep learning-based data fusion framework

that uses trained differentiable surrogates within a Bayesian inference optimization problem to quantify the posterior.

The primary benefit of the data-fusion paradigm is to rapidly adapt pre-trained data-driven models to real-time, potentially

sparse, observations providing increased robustness in nowcasting ability with quantified uncertainty.

We are developing deep learning-based data fusion framework that uses trained differentiable surrogates within a Bayesian

inference optimization problem to quantify the posterior.

The primary benefit of the data-fusion paradigm is to rapidly adapt pre-trained data-driven models to real-time, potentially

sparse, observations providing increased robustness in nowcasting ability with quantified uncertainty.

## Deep Learning-Based

Multimodal Data Fusion

Our project aims to develop advanced methods that will

synergistical exploit multiple, complementing, heterogenous

data sources to estimate true states of large dynamical systems.

We are developing deep learning-based data fusion framework

that uses trained differentiable surrogates within a Bayesian

inference optimization problem to quantify the posterior.

The primary benefit of the data-fusion paradigm is to rapidly

adapt pre-trained data-driven models to real-time, potentially

sparse, observations providing increased robustness in

nowcasting ability with quantified uncertainty.