Abstract: A central challenge in constructing entropy solutions of hyperbolic conservation laws is passing to the limit in nonlinear approximation schemes. Uniform (L^\infty) bounds provide weak compactness, but this alone is insufficient to identify the limit of the nonlinear flux. This talk introduces compensated compactness as a method for overcoming this difficulty by exploiting the entropy structure of the equations. We explain how compactness of entropy production yields constraints on oscillations in approximate solutions and how, under suitable structural assumptions, these constraints lead to strong convergence. We then discuss how this approach can be used to establish the existence of entropy solutions through vanishing viscosity and relaxation approximations. References [1] C. M. Dafermos, Hyperbolic Conservation Laws in Continuum Physics, 4th ed., Springer, 2016. [2] R. J. DiPerna, Compensated compactness and general systems of conservation laws, Trans. Amer. Math. Soc. 292 (1985), 383–420.

In this second talk, we apply the channel of energy method to the nonlinear analysis of radial Type II blow-up for the three-dimensional focusing energy-critical wave equation. We discuss how radiation estimates interact with concentration and compactness to produce rigidity, and how this leads to the classification of possible blow-up profiles. Finally, we relate these ideas to the soliton resolution picture for bounded radial solutions. [References] 1. T. Duyckaerts, C. E. Kenig, F. Merle, Universality of blow-up profile for small radial Type II blow-up solutions of the energy-critical wave equation, J. Eur. Math. Soc. 13 (2011), 533–599. 2. T. Duyckaerts, C. E. Kenig, F. Merle, Classification of radial solutions of the focusing, energy-critical wave equation, Cambridge J. Math. 1 (2013), 75–144.

Singularly perturbed differential equations arise in many problems in fluid mechanics and related areas, where small diffusivity or viscosity can generate sharp boundary, interior, and corner layers. These multiscale structures present significant challenges for both classical numerical methods and standard neural network approaches. We develop neural network methods that use asymptotic analysis to identify and incorporate the dominant structure of singular layers directly into the approximation. By explicitly incorporating the leading singular components into the neural network ansatz, the trainable component is used primarily to approximate the smoother part of the solution. We also consider conservative formulations based on finite volume residuals, which incorporate local flux balance into the learning framework. Numerical examples for a range of singularly perturbed problems demonstrate the accuracy and robustness of these approaches as the perturbation parameter tends to zero.