Modern embedded control architectures have moved from isolated systems to open architectures, such as new automotive systems with services that include remote diagnostics, code updates, and vehicle-to-vehicle communication. However, this increasing set of functionalities, network interoperability, and system design complexity have also introduced security vulnerabilities that are easily exploitable, since current embedded and cyber-physical systems have not been built with security in mind. Furthermore, the tight interaction between information technology and physical world makes these systems vulnerable to malicious attacks beyond the standard cyber-attacks, while relying exclusively on conventional security techniques may be unfeasible due to resource-constraints and long system lifetime.
Consequently, there is a need to change the way we reason about security in cyber-physical systems, and start designing platform-aware attack-resilient components and architectures capable of dealing with various attacks on the systems and its environment. In this talk, I will present our recent efforts in this domain, starting from cyber-physical security techniques that (a) capture effects of attacks on system performance, (b) introduce attack resilience into control algorithms and facilitate attack detection, and (c) enable mapping of the desired Quality-of-Control (QoC) under attack guarantees into real-time performance requirements on the underlying OS and networks. In addition, I will introduce a physics-aware design framework for securing resource-constrained CPS, that supports design-time tradeoffs between QoC in the presence of attacks and system resources used by the deployed security mechanisms, such as message authentication. This design framework has been used to add strong security guarantees in several existing automotive system. Finally, for systems with varying levels of autonomy and human interaction, I will show how we can exploit human power of inductive reasoning and the ability to provide context, to improve the overall security guarantees.
Miroslav Pajic is the Nortel Networks Assistant Professor in Department of Electrical and Computer Engineering, Duke University, with a secondary appointment in the Computer Science Department. He received the Dipl. Ing. and M.S. degrees from the University of Belgrade, Serbia, in 2003 and 2007, as well as the M.S. and Ph.D. degrees from the University of Pennsylvania, Philadelphia, in 2010 and 2012, respectively. His research interests focus on design and analysis of cyber-physical systems (CPS) and in particular on model-based design of CPS, real-time and embedded systems, high-assurance distributed and networked control systems, and high-confidence medical devices and systems.
Dr. Pajic received various awards including NSF CAREER Award, ONR Young Investigator Program Award, ACM SIGBED Frank Anger Memorial Award, Joseph and Rosaline Wolf Best Dissertation Award from Penn Engineering, IBM Faculty Award, as well as six Best Paper and Runner-up Awards, such as the Best Paper Awards at the 2017 ACM SIGBED International Conference on Embedded Software (EMSOFT) and 2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), and the Best Student Paper award at the 2012 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS). He is an associate editor of the ACM Transactions on Computing in Healthcare (ACM HEALTH) and a co-chair of the 2019 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS'19).
We propose a new general framework for building multiscale time-domain decomposition methods. The framework is inspired by the “parareal schemes” introduced by Lions, Maday and Turinici for parallel-in-time computation of evolutionary problems. In this talk, we shall describe our new framework which relies on computed data to build, on-the-fly, effective propagator. We will demonstrate the algorithms that we developed for solving second order wave equations.
Solving large-scale sparse linear systems is an important building block – but often a computational bottleneck – in many science and engineering applications, such as reservoir simulation, Gaussian regression, fluid/solid/structural mechanics and electromagnetics. Most existing solvers fall into two categories: direct methods (e.g., LU and Cholesky) and iterative methods (e.g., CG, MINRES and Multigrid).
This presentation focuses on a parallel hierarchical solver and its applications to a real-world problem – ice sheet modeling. This new solver is based on graph clustering and uses low-rank approximation techniques to sparsify dense fill-in blocks, introduced in the Gaussian elimination process. As a result, it is faster and more memory-efficient than direct solvers for solving large 3D problems. Targeted at distributed-memory machines, the parallel algorithm is based on data decomposition and requires only asynchronous local communication for updating boundary data on every processor.
To demonstrate its robustness, this hierarchical solver is compared with two state-of-the-art pre-conditioners (for iterative solvers), namely the incomplete LU (ILU) factorization and a multigrid solver, for solving linear systems arising from ice sheet modeling. The modeling of thin structures, such as ice sheets, leads to extremely ill-conditioned matrices, which are difficult to solve iteratively. The hierarchical solver, however, converges in almost constant number of iterations if the physical mesh is clustered along horizontal directions. To further improve the efficiency, a stabilized variant of the hierarchical solver was developed.
Dr. Chen received his PhD in computational & mathematical engineering from Stanford in 2018. His PhD research focused on developing fast linear solvers for computational mechanics using the hierarchical matrix theory and parallel computing. At ICES, he is working on fast algorithms for neural-network training with Professor George Biros.
Learning models from data typically means fitting coefficients (weights) of linear combinations of basis (activation) functions to data. In many situations, no particular meaning can be associated with the fitted coefficients of the linear combinations, which means that the learned models predict quantities of interest in black-box ways; however, in science and engineering applications, interpreting models in terms stability, passivity, controllability, attractors, eigenmodes, and other concepts from systems & control theory is critical for guaranteeing the integrity of the overall scientific process. In this presentation, we approach the problem of learning dynamical-system models from the perspective of data-driven model reduction. Instead of fitting linear combinations of basis functions to data, we aim to learn (reduced) operators that describe the dynamics of the systems of interest and so establish notions of systems & control theory concepts for the learned models. We survey recent advances in data-driven model reduction and discuss operator inference and the Loewner framework in detail. Numerical results demonstrate the success of these data-driven model reduction methods and show current limitations and open questions.
Benjamin Peherstorfer is Assistant Professor at Courant Institute of Mathematical Sciences, New York University since 2018. He was Postdoctoral Associate in the Aerospace Computational Design Laboratory (ACDL) at the Massachusetts Institute of Technology (MIT), working with Professor Karen Willcox, and received his Ph.D. degree in computer science from the Technical University of Munich (Germany). Benjamin's current research focus is on computational methods for data- and compute-intensive scientific computing applications, including computational statistics, mathematics of data science, model reduction, uncertainty quantification, and Bayesian inference.
Topology optimization is able to provide unintuitive and innovative design solutions and a performance improvement (e.g. weight savings) in excess of 50% is not uncommonly demonstrated in a wide range of engineering design problems. With the rise of advance materials and additive manufacturing, topology optimization is attracting much attention in the recent years. This presentation will introduce topology optimization in structural design, fiber composites and architected material. It will also include more recent advances topology optimization, multiscale design optimization breaking down the barrier between material and structural designs. Another direction of interests in large-scale topology optimization using the latest sparse data structures tailored to novel level set method. We have demonstrated an order of magnitude improvements on both the memory footage and the computation time. These efforts represent a pathway to applying topology optimization for complex multiphysics multifunctional structures, which may be too complex to rely on designers’ intuition.
Dr. H Alicia Kim is Jacobs Scholar Chair Professor in the Structural Engineering Department of the University of California, San Diego and leads the Multiscale Multiphysics Design Optimization (M2DO) lab. Her interests are in design optimization for structures including level set topology optimization, multiscale optimization, coupled multi physics optimization, modeling and optimization of composite materials and multifunctional structures. She has published around 200 journal and conference papers in these fields including award winning papers at the AIAA conferences and World Congresses on Structural and Multidisciplinary Optimization. Her research in topology optimization began in the 90’s at the University of Sydney, Australia where she developed one of the first boundary based topology optimization methods. She continued her research at the University of Warwick and the University of Bath, UK before moving to the current position in 2015.
We discuss numerical simulations of oceanic internal gravity waves (IGWs) on a global scale, on US Navy, NASA, and European high performance computing platforms. IGWs are waves that exist on the interfaces between oceanic layers of different densities. IGWs of tidal frequency are known as internal tides. Beyond tidal frequencies, there is a spectrum of IGWs known as the IGW continuum. The rollover and breaking of IGWs controls most of the mixing in the open-ocean beneath the mixed layer. IGWs also impact the speed of sound, and yield a measurable sea surface height (SSH) signal. Therefore IGWs are important for satellite altimetry missions, including the upcoming Surface Water and Ocean Topography (SWOT) mission, and for operational oceanography in general. We describe our work with the US Navy HYbrid Coordinate Ocean Model (HYCOM), in which we pioneered high-resolution global ocean models simultaneously forced by atmospheric fields and the astronomical tidal potential. We also examine newer simulations performed under similar conditions, on NASA supercomputers, with the Massachusetts Institute of Technology general circulation model (MITgcm). Finally, we briefly describe related work done with the European ocean forecasting model, the Nucleus for European Modeling of the Oceans (NEMO). We summarize several papers on comparison of the modeled internal tides and the IGW continuum spectrum to altimetry and observations from moorings. We briefly discuss the generation of the continuum spectrum and the potential implications for a better understanding of ocean mixing.