KAUST

This talk is motivated by work on the existence theory for viscoelasticity of Kelvin-Voigt type with non-convex stored energies (joint with K. Koumatos (U. of Sussex), C. Lattanzio and S. Spirito (U. of LAquila)), which shows propagation of H1-regularity for the deformation gradient of weak solutions for semiconvex stored energies. It turns out that weak solutions with deformation gradient in H1 are in fact unique in two-space dimensions, providing a striking analogy to corresponding results in the theory of 2D Euler equations with bounded vorticity.

While weak solutions still exist for initial data in L2, oscillations on the deformation gradi- ent can now persist and propagate in time. This can be seen via a counterexample indicating that for non-monotone stress-strain relations in 1-d oscillations of the strain lead to solutions with sustained oscillations. The existence of sustained oscillations in hyperbolic-parabolic system is then studied in several examples motivated by viscoelasticity and thermoviscoelas- ticity. Sufficient conditions for persistent oscillations are developed for linear problems, and examples in some nonlinear systems of interest. In several space dimensions oscillatory exam- ples are associated with lack of rank-one convexity of the stored energy. Nonlinear examples in models with thermal effects are also developed.

We present a partial differential equation framework for deep residual neural networks and for the associated learning problem. This is done by carrying out the continuum limits of neural networks with respect to width and depth. We study the wellposedness, the large time solution behavior, and the characterization of the steady states of the forward problem. Several useful time-uniform estimates and stability/instability conditions are presented. We state and prove optimality conditions for the inverse deep learning problem, using standard variational calculus, the Hamilton-Jacobi-Bellmann equation and the Pontryagin maximum principle. This serves to establish a mathematical foundation for investigating the algorithmic and theoretical connections between neural networks, PDE theory, variational analysis, optimal control, and deep learning.

This is based on joint work with Hailiang Liu.

The stabilization of thermo-mechanical systems is a classical problem in thermodynamics and well

understood in a context of gases. The objective of this talk is to indicate the role of null-Lagrangians and

certain transport/stretching identities in stabilizing thermomechanical systems associated with general

thermoelastic free energies. This allows to prove various convergence results among thermomechan-

ical theories, and suggests a variational scheme for the approximation of the equations of adiabatic

thermoelasticity.

As Herb Sutter predicted in 2005, "The Free Lunch is Over", software programmers can no longer rely on exponential performance improvements from Moore's Law.  Computationally intensive software now rely on concurrency for improved performance, as at the high end supercomputers are being built with millions of processing cores, and at the low end GPU-accelerated workstations feature hundreds of simultaneous execution cores.  It is clear that the numerical software of the future will be highly parallel, but what language will it be written in?

Over the past few decades, high-level scientific programming languages have become an important platform for numerical codes. Languages such as MATLAB, IDL, and R, offer powerful advantages: they allow code to be written in a language more familiar to scientists and they permit development to occur in an evolutionary fashion, bypassing the relatively slow edit/compile/run/plot cycle of Fortran or C. Because a scientist’s programming time is typically much more valuable than the computing cycles their code will use, these are substantial benefits. However, programs written in such languages are not portable to high performance computing platforms and may be too slow to be useful for realistic problems on desktop machines. Additionally, the development of such interpreted language codes is partially wasteful in the sense that it typically involves reimplementation (with associated debugging) of some algorithms that already exist in well-tested Fortran and C codes.  Python stands out as the only high-level language with both the capability to run on parallel supercomputers and the flexibility to interface with existing libraries in C and Fortran.

Our code, PyClaw, began as a Python interface, written by University of Washington graduate student Kyle Mandli, to the Fortran library Clawpack, written by University of Washington Professor Randy LeVeque.  PyClaw was designed to build on the strengths of Clawpack by providing greater accessibility.  In this talk I will describe the design and implementation of PyClaw, which incorporates the advantages of a high-level language, yet achieves serial performance similar to a hand-coded Fortran implementation and runs on the world's fastest supercomputers. It brings new numerical functionality to Clawpack, while making maximal reuse of code from that package.  The goal of this talk is to introduce the design principles we considered in implementing PyClaw, demonstrate our testing infrastructure for developing within PyClaw, and illustrate how we elegantly and efficiently distributed problems over tens of thousands of cores using the PETSc library for portable parallel performance.  I will also briefly highlight a new mathematical result recently obtained from PyClaw, an investigation of solitary wave formation in periodic media in 2 dimensions.

Strong explosive volcanic eruptions could inject in the lower stratosphere million tons of SO2, which being converted to sulfate aerosols, affect radiative balance of the planet for a few years. During this period the volcanic radiative forcing dominates other forcings producing distinct detectable climate responses. Therefore volcanic impacts provide invaluable natural test of climate nonlinearities and feedback mechanisms. In this talk I will overview volcanic impacts on tropospheric and strsatospheric temperature, ozone, high-latitude circulation, stratosphere-troposphere dynamic interaction, and focus on the long-term volcanic effect on ocean heat content and sea level.