Seminari - Dipartimento Informatica validi dal 31.08.2026 al 31.08.2027. https://www.di.univr.it/?ent=seminario&rss=0 https://www.di.univr.it/?ent=seminario&rss=0&id=7071 Relatore: Shi Jin; Provenienza: Shanghai Jiao Tong University; Data inizio: 2026-08-31; Ora inizio: 11.00; Note orario: Sala Verde; Referente interno: Giacomo Albi; Riassunto: Quantum computers are designed based on quantum mechanics principle, they are most suitable to solve the Schrodinger equation, and linear PDEs (and ODEs) evolved by unitary operators. Nonlinearity and Nonunitarity are the main challenges for quantum simulations of PDEs. For linear PDEs (and ODEs) Schroginerization provides a general method to unitarize them for quantum simulation. For nonlinear problems we first introduce our quantum algorithms for (nonlinear) Hamiton-Jacobi equations, for both multi-valued solutions and viscosity solutions that are needed beyond the formation of caustics. We also introduce quantum algorithms for Young measures associated with nonlinear PDEs, which are effective tools to compute weak solutions to nonlinear PDEs that have singular solutions such as shocks, caustics, physical instabilities or (random) uncertainties. We also introduce ldquo;UnitaryLabrdquo;, which is an AI-powered research software package of quantum algorithms for scientific computing. Streaming of the seminar will be possible upon request by emailing: giacomo.albi@univr.it . Mon, 31 Aug 2026 11:00:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=7071 https://www.di.univr.it/?ent=seminario&rss=0&id=7072 Relatore: Eduardo D. Sontag; Provenienza: Northeastern University, Boston, USA; Data inizio: 2026-09-07; Ora inizio: 10.30; Note orario: Sala Verde (solo presenza); Referente interno: Paolo Dai Pra; Riassunto: ABSTRACT : Solutions of optimization problems, including policy optimization in reinforcement learning, typically rely upon some variant of gradient descent. There has been much recent work in the machine learning, control, and optimization communities applying the Polyak-Łojasiewicz Inequality (PŁI) to such problems in order to establish an exponential rate of convergence (a.k.a. ldquo;linear convergencerdquo; in the local-iteration language of numerical analysis) of loss functions to their minima under the gradient flow. Often, as is the case of policy iteration for the continuous-time LQR problem, this rate vanishes for large initial conditions, resulting in a mixed globally linear / locally exponential behavior. This is in sharp contrast with the discrete-time LQR problem, where there is global exponential convergence. That gap between CT and DT behaviors motivates the search for various generalized PŁI-like conditions, and this talk will address that topic. Moreover, these generalizations are key to understanding the transient and asymptotic effects of errors in the estimation of the gradient, errors which might arise from adversarial attacks, wrong evaluation by an oracle, early stopping of a simulation, inaccurate and very approximate digital twins, stochastic computations (algorithm quot;reproducibilityquot;), or learning by sampling from limited data. We will describe an ldquo;input to state stabilityrdquo; (ISS) analysis of this issue. We will also discuss convergence and PŁI-like properties of ldquo;linear feedforward neural networksrdquo; in feedback control. (Joint work with A.C.B. de Oliveira, L. Cui, Z.P. Jiang, and M. Siami). . Mon, 7 Sep 2026 10:30:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=7072