Schools and Workshops
IMPRS Summer School 2024  Wroclaw, Poland from July 2931, 2024
'Machine learning and Many body systems in or out of equilibrium'
The IMPRS summer school 2024 took place in Wroclaw / Poland. The event was cohosted by the Insitute of Low Temperature and Structure Research and the University of Wroclaw.
Materials and Photos:
Here you will find the materials of the lectures as well as some photos that were taken during the event.
08:15 am  Welcome and Introduction Organizers: Tomasz Zaleski, Institute of Low Temperature and Structure Research; David Blaschke, University of Wroclaw; Armen Sedrakjan, University of Wroclaw; Panos Giannakeas, MPIPKS, Dresden;  
08:30 am  Time Crystals  Part I Speaker: Prof. Krzysztof Sacha (Institute of Theoretical Physics Jagiellonian University in Krakow)  
10:00 am  Coffee break  
10:30 am  The nonequilibrium realtime Green’s functions techniques and its applications Speaker: Prof. Armen Sedrakjan (University of Wroclaw)  
12:00 pm  Lunch  
01:00 pm  The method of the nonequilibrium statistical operator and its applications Speaker: Prof. Armen Sedrakjan (University of Wroclaw)  
02:30 pm  Coffee break  
03:00 pm  Hands on Introduction to Deep Reinforcement Learning for Qubit Manipulation  Part I Speaker: Dr. Marin Bukov, MPIPKS, Dresden  
04:30 pm  Coffee break  
05:00 pm  Hands on Introduction to Deep Reinforcement Learning for Qubit Manipulation  Part II Speaker: Dr. Marin Bukov, MPIPKS, Dresden  
06:30 pm  Dinner at Hotel Wodnik 
09:00 am  Time Crystals  Part II Speaker: Prof. Krzysztof Sacha (Institute of Theoretical Physics Jagiellonian University in Krakow)  
10:30 am  Coffee break  
11:15 am  Surrogate modeling of laser plasma interaction using machine learning: Data Speaker: Dr. Michael Bussmann (HelmholtzZentrum DresdenRossendorf)  
01:00 pm  Lunch at the restaurant Karczma Lwowska Phone: +48 71 343 98 87
 
03:00 pm  Introduction to Deep Learning Speaker: Dr. Attila Cangi (HelmholtzZentrum DresdenRossendorf)  
04:30 pm  Coffee break  
05:00 pm  Solving Differential Equations with Machine Learning Speaker: Dr. Attila Cangi (HelmholtzZentrum DresdenRossendorf)  
06:30 pm  Discussions  
07:30 pm  Dinner at the restaurant Piwnica Świdnicka Rynek Ratusz 1A, 50106 Wrocław, PolenPhone: +48 538 508 864 piwnicaswidnicka.pl/en/homeen/

08:30 am  Surrogate modeling of laser plasma interaction using machine learning: Methods Speaker: Dr. Michael Bussmann (HelmholtzZentrum DresdenRossendorf)  
10:00 am  Coffee break  
10:30 am  BCS–BEC crossover in ultracold gases and nuclear systems Speaker: Prof. Gabriel Wlazłowski (Warsaw University of Technology)  
12:00 pm  Lunch  
01:00 pm  Density Functional Theory for systems with pairing correlations Speaker: Prof. Gabriel Wlazłowski (Warsaw University of Technology)  
02:30 pm  Coffee break  
03:00 pm  Macroscopic description of manybody systems  Part I Speaker: Prof. Piotr Surowka (Wrocław University of Science and Technology)  
04:30 pm  Coffee break  
05:00 pm  Macroscopic description of manybody systems  Part II Speaker: Prof. Piotr Surowka (Wrocław University of Science and Technology)  
06:30 pm  Wrapup & Dinner at Hotel Wodnik Organizers: Thomasz Zaleski, Institute of Low Temperature and Structure Research; David Blashke, University of Wroclaw; Armen Sedrakjan, University of Wroclaw; Panos Giannakeas, MPIPKS, Dresden; Kristin Paske, MPIPKS, Dresden 
Time Crystals (Speaker: Prof. Krzysztof Sacha  Institute of Theoretical Physics Jagiellonian University)
The lectures provide a comprehensive overview of time crystals, which exhibit a repeating temporal structure. They introduce fundamental concepts related to time crystals and explore various branches of this emerging research area. The lectures begin with the original idea of time crystallization in quantum systems introduced by Wilczek and present the development of this field up to the present day. They describe both the spontaneous formation of time crystalline structures and the concepts of condensed matter physics in the time domain, from Anderson localization in time to manybody systems with exotic interactions. The lectures also outline the prospect of timetronics development, which involves building useful devices where time crystalline structures play a crucial role.
The nonequilibrium realtime Green’s functions techniques and its applications // The method of the nonequilibrium statistical operator and its applications (Speaker: Prof. Armen Sedrakjan  University of Wroclaw)
In the first lecture I will develop the realtime Green’s functions techniques. I will derive in this formalism the transport equations known as KadanoffBaym equations and will illustrate their applications via the evaluation of the collision integrals of several example systems, ranging from a dilute Bose gas to strongly interacting nuclear matter. Numerical examples will be given. The second lecture will develop an alternative nonequilibrium method known as the method of the nonequilibrium statistical operator. It will allow us to evaluate of transport properties of strongly correlated systems via KuboGreen formulas for correlation functions. As an illustration, the method will be applied to quarkgluon plasma in the framework of the NambuJonaLasinio model of fourfermion interaction. Numerical examples of computation of the transport coefficient of quarkgluon plasma, such as the shear viscosity, will be given.
Hands on Introduction to Deep Reinforcement Learning for Qubit Manipulation (Speaker: Dr. Marin Bukov, MPIPKS)
Reinforcement learning (RL) is one of the three modern pillars of modern machine learning, and constitutes the "learning from interactions" paradigm. Applications of RL include superhuman performance in playing video and board games, robotic steering and locomotion, control of physical systems, and many others. The two lectures will provide a brief handson introduction to deep reinforcement learning for quantum control. After a short overview of the topic to define the main concepts, we will consider the specifics of the RL framework for quantum control on NISQ and AMO devices. In particular, we will discuss how to incorporate quantum mechanical constraints such as the nonmeasurability of quantum states, the backaction on the system from strong measurements, and the binary nature of quantum data. As a specific example, we will learn how to manipulate the stochastic dynamics of a noisy qubit by coupling it to an ancilla.
We will use Jupyter notebooks and the JAX package [https://jax.readthedocs.io/en/latest/].
Surrogate modeling of laser plasma interaction using machine learning: Data (Speaker: Dr. Michael Bussman  HelmholtzZentrum DresdenRossendorf)
In this lecture we will introduce the role of data in modeling laser plasma interaction using machine learning. We will motivate the use of machine learning by explaining the challenges of modeling laser plasma interactions at high intensities, introducing the state of the art modeling techniques. We will discuss the various data sources and the challenges in making them useful for machine learning. We dive deeper into the role of nonlinear interactions in both experiments and simulations. We finally present a path forward using synthetic diagnostics and analysis routines for nonconvex inverse problems to connect experimental measurements with simulation data. We conclude by presenting important ingredients in successfully using machine learning for modeling laser plasma interaction at high intensities.
Introduction to Deep Learning (Speaker: Dr. Attila Cangi  HelmholtzZentrum DresdenRossendorf)
In this lecture I will introduce the concept of neural networks. We will begin with a brief overview of the development of artificial neural networks. We will look at the basic perceptron model from a mathematical point of view and implement it to solve a simple classification problem. In the last part of the lecture, I will provide a gentle interactive introduction to deep learning using a simple toy problem about digital colors. We will learn how to build neural network pipelines and develop a qualitative understanding. The lecture will be both formal and interactive using Jupyter notebooks.
Solving Differential Equations with Machine Learning (Speaker: Dr. Attila Cangi  HelmholtzZentrum DresdenRossendorf)
In this lecture I will show how neural networks can be used to solve differential equations. We will consider the basic example of the quantum harmonic oscillator. After reviewing some basic concepts, we will implement two machine learning methods to solve the timedependent Schrödinger equation for the harmonic oscillator. First, we will consider a datadriven approach where a fully connected neural network is used to learn the solutions of the differential equation based on input labels. In the second approach, we will consider physicsinformed neural networks. In contrast to the datadriven approach, the solution of the differential equation is not learned by mapping input features to outputs, but by minimizing a loss term related to the form of the differential equation. The lecture will be both formal and interactive using Jupyter notebooks.
Surrogate modeling of laser plasma interaction using machine learning: Methods (Speaker: Dr. Michael Bussman  HelmholtzZentrum DresdenRossendorf)
In this second part we focus on the technologies used for modeling laser plasma interaction. Among these are
 Bayesian modeling techniques for linear optimization problems for few shot experiments
 Generative models such as variational autoencoders to efficiently reduce the complexity of laser matter interactions
 Generative models such as normalizing flows to model highly nonlinear behavior
 Invertible neural networks to connect observables with initial conditions
Which we will put in context with some important applications in laser matter interaction. We conclude with an outlook on the role of foundation models, MLOPS, data repositories, metadata and ontologies
BCS–BEC crossover in ultracold gases and nuclear systems (Speaker: Prof. Gabriel Wlazłowski  Warsaw University of Technology)
The lecture introduces the BCS (Bardeen–Cooper–Schrieffer)–BEC (BoseEinstein condensation) crossover phenomenon, which will be used as a platform for comprehensive studies of superfluidity in bosonic and fermionic systems. A survey of experimental progress in the realization of BCSBEC crossover with ultracold atomic gases will be provided. Next, the main theoretical frameworks for describing bosonic and fermionic superfluids will be discussed. The emphasis will be put on meanfield methods: the Bogoliubov–de Gennes theory for Fermi gases and the Gross–Pitaevskii equation for Bose systems. In the last part of the lecture, the relation of the BCSBEC crossover to nuclear systems will be highlighted. In particular, similarities and differences to superfluid neutron matter will be explained under conditions as expected in neutron stars.
Density Functional Theory for systems with pairing correlations (Speaker: Prof. Gabriel Wlazłowski  Warsaw University of Technology)
The lecture will introduce Density Functional Theory (DFT) extended to superfluid systems. The DFT approach overcomes the limitations of meanfield methods and can be applied to strongly interacting systems, like unitary Fermi gas or nuclear matter. The fundamental theorems constituting the method will be explained, together with the methodology of constructing energy density functional for superfluid systems and the concept's practical implementation. Next, an extension of the method to a timedependent variant will be presented. Finally, examples of applications of the method to various systems will be provided, which include dynamics of superfluid Fermi gases or neutron matter, quantum vortices, nuclear reactions, and quantum turbulence.
Macroscopic description of manybody systems (Speaker: Prof. Piotr Surowka  Wroclaw University of Science and Technology)
Neural networks have demonstrated exceptional performance in classification tasks involving structured, highdimensional datasets. However, their macroscopic description remains poorly understood. It is widely believed that hydrodynamics offers a macroscopic description of manybody physics. Constructing the hydrodynamic, macroscopic description of neural networks is, therefore, a crucial step towards our understanding of these systems.
In these lectures, we will present stateoftheart knowledge about constructing hydrodynamic theories based on symmetries of the underlying manybody state. We will discuss a general approach, based on classical Poisson brackets, that facilitates the automatic and straightforward derivation of hydrodynamic equations for any manybody system with spontaneously broken symmetries. In the final part of the lecture, we will illustrate the connection between these domains by providing an example of the hydrodynamic description of a simple binary neural network.