Applied Mathematics Colloquium - Alex Townsend

Jan. 27, 2023

Alex Townsend, Department of Mathematics, Cornell University The art and science of low-rank techniques Matrices and tensors that appear in computational mathematics are so often well-approximated by low-rank objects. Since random ("average") matrices are almost surely of full rank, mathematics needs to explain the abundance of low-rank structures. We will...

Applied Mathematics Colloquium - Daniel Rothman

Dec. 8, 2022

Daniel Rothman; Department of Earth, Atmospheric, and Planetary Sciences; Massachusetts Institute of Technology Excitations of Earth's Carbon Cycle Mysterious, transient changes in the ocean's store of carbon occur intermittently throughout Earth's history. Each of these events coincides with climate change; moreover, mass extinctions are always accompanied by such events. What...

Applied Mathematics Colloquium - Stephanie Chaillat

Dec. 2, 2022

Stephanie Chaillat, Laboratoire POems, École Nationale Supérieure de Techniques Avancées Paris Fast Boundary Element Methods to simulate underwater explosions and their interactions with submarines Assessing the impact of underwater explosions on submerged structures (submarines) is an important naval engineering problem. An underwater explosion mainly induces two distinct phenomena: a "shock...

Applied Mathematics Colloquium - Robert MacCurdy

Nov. 11, 2022

Robert MacCurdy, Department of Mechanical Engineering, University of Colorado Boulder Automated Design and Fabrication of Multimaterial Soft Robots Current electromechanical design practice is predicated on the exercise of expert-level judgement through an interactive and iterative design and fabrication process that requires skilled humans at every step. This approach doesn't scale...

Applied Mathematics Colloquium - Heather Wilber

Nov. 4, 2022

Heather Wilber, Oden Institute, University of Texas at Austin Rational functions in computational mathematics From dynamical systems and signal processing theory to core algorithms in numerical linear algebra, rational approximation theory has always shaped the way we think about computational mathematics. Even so, outside of a few very active areas,...

Applied Mathematics Colloquium - Mark Ablowitz

Oct. 28, 2022

Mark Ablowitz, Department of Applied Mathematics, University of Colorado Boulder Looking Back: Reflections on Research and Teaching Mark J. Ablowitz is the recipient of the 2022 Hazel Barnes Prize. This is the most prestigious single faculty award funded by the University of Colorado Boulder. It was established in 1991 to...

Applied Mathematics Colloquium - Peter Hamlington

Oct. 21, 2022

Peter Hamlington, Department of Mechanical Engineering, University of Colorado Boulder The Structure and Dynamics of Puffing Plumes The near-field characteristics of highly buoyant plumes, commonly referred to as lazy plumes, remain relatively poorly understood across a range of flow conditions, particularly compared to our understanding of far-field characteristics. Buoyant plumes...

Applied Mathematics Colloquium - Dorit Hammerling

Oct. 14, 2022

Dorit Hammerling, Department of Applied Mathematics and Statistics, Colorado School of Mines Lossy Data Compression and the Community Earth System Model Climate models such as the Community Earth System Model (CESM) typically produce enormous amounts of output data, and storage capacities have not increased as rapidly as processor speeds over...

Applied Mathematics Colloquium - Bri-Matthias Hodge

Oct. 7, 2022

Bri-Mathias Hodge; Department of Electrical, Computer, and Energy Engineering; University of Colorado Boulder Designing a Sustainable and Reliable Future: Simulating Next Generation Energy Systems Power and energy systems worldwide are changing rapidly in the face of the global challenge of decarbonization and the subsequent addition of more distributed and variable...

Applied Mathematics Colloquium - Steffen Borgwardt

Sept. 30, 2022

Steffen Borgwardt, Department of Mathematics, University of Colorado Denver Transitions between Clusterings Clustering is one of the fundamental tasks in data analytics and machine learning. In many situations, different partitions of the same data set become relevant. For example, different algorithms for the same clustering task may return dramatically different...

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