-
Matt Colbrook
Mon Nov 19, 2018
4:00 pm
We will discuss and extend the Solvability Complexity Index (SCI) hierarchy, which is a classification hierarchy for all types of problems in computational mathematics that allows for classifications determining the boundaries of what computers can achieve in scientific computing. The SCI hierarchy captures many key computational issues in the...
-
Bo Li
Mon Nov 5, 2018
4:00 pm
The ligand-receptor binding/unbinding is a complex biophysical process in which water plays a critical role. To understand the fundamental mechanisms of such a process, we have developed a new and efficient approach that combines our level-set variational implicit-solvent model with the string method for transition paths, and have studied the...
-
Amir Sagiv
Mon Oct 29, 2018
4:00 pm
The control and prediction of interactions between high-power, nonlinear laser beams is a longstanding open problem in optics and mathematics. One of the traditional assumptions in this field has been that these interactions are deterministically modelled by the nonlinear Schrodinger equation (NLS). Lately, however, we have shown that at the...
-
Louis Komzsik
Mon Oct 15, 2018
4:00 pm
Engineering topology optimization is a technique to minimize the mass of a structure while maintaining or even increasing its robustness in certain lifecycle applications. The presentation will show the technical foundation based on the finite element method with an embedded gradient based mathematical optimizer. Characteristic...
-
Eberhard Voit
Mon Oct 1, 2018
4:00 pm
Most biological experiments result in snapshots that illustrate select aspects of a phenomenon of interest. These snapshots may be very complicated, consisting of thousands of peaks in a mass spectrogram, the expression levels of whole genomes, or an impressive image visualizing the localization of different proteins. Yet, each result is...
-
Bao Wang
Mon May 14, 2018
4:00 pm
First, I will present the Laplacian smoothing gradient descent proposed recently by Prof. Stan Osher. We show that when applied to a variety of machine learning models including softmax regression, convolutional neural nets, generative adversarial nets, and deep reinforcement learning, this very simple surrogate of gradient descent can...
-
Jay Newby
Thu Apr 26, 2018
10:00 am
The longstanding view in chemistry and biology is that high-affinity, tight-binding interactions are optimal for many essential functions, such as receptor-ligand interactions. Yet, an increasing number of biological systems are emerging that challenge this view, finding instead that low-affinity, rapidly unbinding dynamics can be essential for...
