InfoSymbiotics/DDDAS2020

Agenda

Presentations

** LNCS Proceedings: Third International Conference, DDDAS 2020, Boston, MA, USA, October 2-4, 2020 (LNCS 12312) **

The Dynamic Data Driven Applications Systems (DDDAS)/InfoSymbiotics2020 (or “DDDAS2020”) conference showcases scientific research advances and technology capabilities stemming from the Dynamic Data Driven Applications Systems (DDDAS) paradigm, whereby instrumentation data are dynamically integrated into an executing application model and in reverse, the executing model controls the instrumentation. DDDAS/InfoSymbiotics plays a key role in advancing capabilities in many application areas – in aerospace, materials sciences, biosciences, geosciences and space sciences, resilient security, and cyber systems for critical infrastructures. In addition, DDDAS is also driving advances in foundational methods, through system-level (as well as subsystems-level) representations, that include comprehensive principle- and physics-based-models and instrumentation, uncertainty quantification, estimation, observation, sampling, planning and control. The scope of application areas ranges from the nano-scale to the extra-terra-scale. The conference is a forum to present and discuss such methods and advances, as per the conference Agenda.

Keynote Speaker: Sangtae Kim, Purdue University

Title: Revisiting the Top Ten Ways that DDDAS Can Save the World – With an update in the BioInfoSciences area and on the Energy Bridge

Abstract: Three years ago (DDDAS Symposium at the 2017 ASME Meeting) we looked at the speaker’s “top ten” list of how DDDAS “can save the world”. Now as we adjust to life under the COVID-19 pandemic and a 2020 Conference in the virtual format, our world literally seeks rescue/saving. Under these circumstances, we revisit the top ten list and first consider briefly the dynamic data-driven aspects of the COVID-19 challenges from the speaker’s experiences in the biotech/pharma industry, before providing an update on the Energy Bridge area. [Bio Overview]

Keynote Speaker: Michael Seablom, NASA

Title: New Observing Strategies for NASA Science

Abstract: NASA’s Earth Science Technology Office (ESTO) is prioritizing a “New Observing Strategy” to help mitigate the risk, cost, size and development time of future Earth Science missions and their corresponding information systems and to increase the use of NASA’s Earth Science data. The strategy consists of exploiting distributed spacecraft, constellations, and “sensor webs” to enable new observation measurements and information products. Although the overarching concept has been studied for nearly 20 years, the emergence of low-cost small spacecraft through commercial platforms and high quality, miniaturized science instruments makes possible domain-specific scientific investigations. When coupled with numerical prediction models, these types of spacecraft will enable new scientific investigations of phenomena that previously could not have been studied or would have been too expensive to study. [Bio Overview]

Keynote Speaker: Karen Willcox, UT Austin

Title: Predictive digital twins: Where dynamic data-driven learning meets physics-based modeling

Abstract: A digital twin is an evolving virtual model that mirrors an individual physical asset throughout its lifecycle. An asset-specific model is a powerful tool to underpin intelligent automation and drive key decisions. The formulations and methods of DDDAS have a key role to play in the tasks of inference, assimilation, prediction, control, and planning that enable the digital twin paradigm. Of particular importance is a tight feedback loop between models and data, which has long been a central concept in DDDAS. This talk presents an approach to create, update, and deploy data-driven physics-based digital twins. We demonstrate the approach through the development of a structural digital twin for a custom-built unmanned aerial vehicle. We use the digital twin for dynamic in-flight decision-making to replan a safe mission in response to vehicle structural damage. The predictive digital twin is built from a library of component-based reduced-order models that are derived from high-fidelity finite element simulations of the vehicle in a range of pristine and damaged states. The digital twin is deployed and updated using interpretable machine learning. Specifically, we use optimal classification trees to train a scalable and interpretable data-driven classifier. In operation, the classifier takes as input vehicle sensor data, and then infers which physics-based reduced models in the model library are the best candidates to compose an updated digital twin. [Bio Overview]

Keynote Speaker: Dr. Ilkay Altintas, San Diego Supercomputer Center

Title: Using Dynamic Data Driven Cyberinfrastructure for Next Generation Disaster Intelligence

Abstract: Wildland fires and related hazards are increasing globally. A common observation across these large events is that fire behavior is changing to be more destructive, making applied fire research more important and time critical. Significant improvements towards modeling of the extent and dynamics of evolving plethora of fire related environmental hazards, and their socio-economic and human impacts can be made through intelligent integration of modern data and computing technologies with techniques for data management, machine learning and fire modeling. However, there are still challenges and opportunities in integration of the scientific discoveries and data-driven methods for hazards with the advances in technology and computing in a way that provides and enables different modalities of sensing and computing. The WIFIRE cyberinfrastructure took the first steps to tackle this problem with a goal to create an integrated system, data and visualization services, and workflows for wildfire monitoring, simulation, and response. Today, WIFIRE provides an end-to-end management infrastructure from the data sensing and collection to artificial intelligence and modeling efforts using a continuum of computing methods that integrate edge, cloud, and high-performance computing. Though this cyberinfrastructure, the WIFIRE project provides data driven knowledge for a wide range of public and private sector users enabling scientific, municipal, and educational use. This talk will review some of our recent work on building this dynamic data driven cyberinfrastructure and impactful application solution architectures that showcase integration of a variety of existing technologies and collaborative expertise. [Bio Overview]

Keynote Speaker: Dr. Irene Gregory, NASA Langley Research Center

Title: Intelligent Contingency Management for Urban Air Mobility

Abstract: The advent of third aviation revolution that is seeking to enable transportation where users have access to immediate and flexible air travel. The users dictate trip origin, destination and timing. One of the major components of this vision is urban air mobility (UAM) for the masses. UAM means a safe and efficient system for vehicles to move passengers and cargo within a city. In order to reach UAM’s full market potential the vehicle will have to be autonomous. One of the primary challenges of autonomous flight is dealing with off-nominal events, both common and unforeseen; thus, intelligent contingency management (ICM) is one of the enabling technologies. In this context, the vehicle has to be aware of its internal state and external environment at all times, ascertain its capability and make decisions about mission completion or modification. All of these functions require data to model and assess the environment and then take actions based on these models. Necessarily, there is uncertainty associated with the data and the models generated from it. Since we are dealing with safety-critical systems, one of the main challenges of ICM is to generate sufficient data and to minimize its uncertainty to enable practical and safe decision making. We propose an overall architecture that incorporates deterministic and learning algorithm together to assess vehicle capabilities, project these into the future and make decision on mission management level. A layered approach allows for mature parts and technologies to be integrated into early highly automated vehicle before the final state of autonomy is reached. [Bio Overview]

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