Today, both papers look at issues central in engineering desing. The first one focuses on developing randomized methods to accelerate the computations of PDEs with a large number of variables. The second paper looks at engineering from the experimental standpoint and tries to evaluate fields from scattered networks of sensors.
Randomized Alternating Least Squares for Canonical Tensor Decompositions: Application to a PDE with Random Data by Matthew Reynolds, Alireza Doostan, Gregory Beylkin
This paper introduces a randomized variation of the alternating least squares (ALS) algorithm for rank reduction of canonical tensor formats. The aim is to address the potential numerical ill-conditioning of least squares matrices at each ALS iteration. The proposed algorithm, dubbed randomized ALS, mitigates large condition numbers via projections onto random tensors, a technique inspired by well-established randomized projection methods for solving overdetermined least squares problems in a matrix setting. A probabilistic bound on the condition numbers of the randomized ALS matrices is provided, demonstrating reductions relative to their standard counterparts. Additionally, results are provided that guarantee comparable accuracy of the randomized ALS solution at each iteration. The performance of the randomized algorithm is studied with three examples, including manufactured tensors and an elliptic PDE with random inputs. In particular, for the latter, tests illustrate not only improvements in condition numbers, but also improved accuracy of the iterative solver for the PDE solution represented in a canonical tensor format.
Sparse sensing and DMD based identification of flow regimes and bifurcations in complex flows
Boris Kramer, Piyush Grover, Petros Boufounos, Mouhacine Benosman, Saleh Nabi
We present a sparse sensing and Dynamic Mode Decomposition (DMD) based framework to identify flow regimes and bifurcations in complex thermo-fluid systems. Motivated by real time sensing and control of thermal fluid flows in buildings and equipment, we apply this method to a Direct Numerical Simulation (DNS) data set of a 2D laterally heated cavity, spanning several flow regimes ranging from steady to chaotic flow. We exploit the incoherence exhibited among the data generated by different regimes, which persists even if the number of measurements is very small compared to the dimension of the DNS data. We demonstrate that the DMD modes and eigenvalues capture the main temporal and spatial scales in the dynamics belonging to different regimes, and use this information to improve the classification performance of our algorithm. The coarse state reconstruction obtained by our regime identification algorithm can enable robust performance of low order models of flows for state estimation and control.
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