Identifying whether a process is in equilibrium, quantifying how far it lies from equilibrium, and determining optimal reduced descriptions of non-equilibrium processes remain challenging open problems. Here, we discuss how novel data-driven techniques grounded in stochastic thermodynamics can be used to efficiently learn these features directly from experimental data. In particular, we show how entropy production can be localized in space and time, and how maximally dissipative coordinates can be consistently inferred as effective low-dimensional descriptions of non-equilibrium processes. We further discuss applications to experimental biophysical systems and outline key challenges and limitations.
Schematic illustration of the light-momentum detection principle underlying SmartTrap. The momentum change of the trapping laser, induced by its interaction with the trapped particle, is measured to directly quantify optical forces with high precision, enabling real-time feedback and autonomous control in non-equilibrium experiments. (Figure by A. Ciarlo.)SmartTrap: Autonomous Optical Tweezers for Statistical Physics of Non-Equilibrium Systems
Antonio Ciarlo Date: 26th February 2026 Time: 13.30 Place: NORDITA, Stockholm, Sweden The 15th Nordic Workshop on Statistical Physics: Biological, Complex and Non-equilibrium Systems
Optical tweezers are a key tool in non-equilibrium statistical physics, allowing direct measurements of forces, work, and fluctuations in single-molecule and soft matter systems. However, manual operation limits throughput and the systematic study of rare events.
In this talk, Antonio Ciarlo will present SmartTrap, a fully autonomous optical tweezers platform integrating deep learning–based 3D tracking, adaptive feedback control, and automated microfluidics. The system operates without human intervention, executing complete force spectroscopy protocols.
Demonstrated with high-throughput DNA pulling experiments on λ-DNA, SmartTrap enables precise measurements of force–extension curves and folding kinetics. The platform also opens new possibilities for studies of colloids, single cells, and quantitative tests of non-equilibrium statistical physics.
Active Matter: Model Systems and Experimental Tests
Agnese Callegari, Antonio Ciarlo, Sreekanth Manikandan Dates and times:
23 Feb 14:00-15:00 (Agnese)
24 Feb 11:30-12:30 (Antonio)
24 Feb 14:00-15:00 (Sreekanth) Place: PJ Winter school on Geometry of nonequilibrium critical phenomena
Active matter is a broad class of systems that operate intrinsically out of equilibrium. It spans multiple length scales—from macroscopic to micro- and nanoscopic—and includes both biological and artificial realizations, often displaying rich and emerging collective behaviors. The study of active matter aims to explain and interpret these phenomena using concepts and tools from physics. As such, understanding active and non-equilibrium systems requires a combination of theoretical, computational, and experimental approaches.
In the first part of the lecture, we introduce the concept of an active particle and demonstrate how it can be embodied in a macroscopic, self-propelled toy robot (a Hexbug). Despite their simplicity, such systems reproduce characteristic—and sometimes counterintuitive—features of microscopic active matter. These experiments have a strong pedagogical value and are designed to help bridge a gap in traditional physics curricula at the primary and secondary education levels.
The second part of the lecture focuses on active matter and non-equilibrium phenomena at the microscopic scale, where advanced experimental tools are essential. Optical tweezers provide precise control over microscopic systems and access to key physical observables. We introduce their operating principles and illustrate how they can be used to construct a minimal, well-controlled experimental model for studying non-equilibrium dynamics at the single-particle level.
In the final part of the lecture, we turn to the theoretical and computational tools required to analyze active matter systems. We discuss how non-equilibrium dynamics can be quantitatively characterized directly from experimental data in a model-independent framework. This naturally leads to an introduction to machine-learning–based inference techniques, which extract dynamical and thermodynamic information from data without relying on a priori assumptions about the underlying physical model.
References:
[1] A. Barona Balda, A. Argun, A. Callegari, G. Volpe. Playing with Active Matter, Am. J. Phys. 92, 847–858 (2024). https://doi.org/10.1119/5.0125111
[2] Martins, T.T., Malavazi, A.H.A., Kamizaki, L.P. et al. Fluctuation theorems with optical tweezers: theory and practice. Eur. Phys. J. Plus 141, 71 (2026). https://doi.org/10.1140/epjp/s13360-025-07181-4
[3] Manikandan, Sreekanth K. and Ghosh, T. and Mandal, T. and Biswas, A. and Sinha, B. and Mitra, D. Estimate of entropy production rate can spatiotemporally resolve the active nature of cell flickering. Phys. Rev. Res. 6, 023310 (2024). https://doi.org/10.1103/PhysRevResearch.6.023310
Photos
Antonio, presenting. (Photo by M. Orsino)Sreekanth, presenting. (Photo by A. Ciarlo)
Hang Zhao, supervised by Giovanni Volpe and Joana Pereira, will present his halftime seminar under the topic “Brain connectome revealed neuro-degenerative disease” on 9-10 am, 22nd Jan. 2026 in Nexus and through Zoom (https://gu-se.zoom.us/j/7726618257). The seminar starts from his presentation about the past and planned project, followed by a discussion and questions by his opponent, Professor Mattias Göksor.
Quantifying the spatiotemporal forces, affinities, and dissipative costs of cellular-scale non-equilibrium processes from experimental data and localizing it in space and time remain a significant open challenge. Here, I explore how principles from stochastic thermodynamics, combined with machine learning techniques, offer a promising approach to addressing this issue. I will present preliminary results from experiments on fluctuating cell membranes and simulations of non-equilibrium systems in stationary and time-dependently driven states. These studies reveal potential strategies for localizing entropy production in experimental biophysical contexts while also highlighting key challenges and limitations that must be addressed.
Massimiliano Passaretti (left) and Yu-Wei Chang (right) at NEME 2025. (Photo courtesy of Clarion Hotel Draken.)Graph theory and deep learning pipelines
Yu-Wei Chang, Massimiliano Passaretti NEMES 2025, 24-26 September, 2025 Date: 25 September 2025 Time: 12:45 – 14:00 Place: Clarion Hotel Draken
This workshop begins with a practical introduction to graph theory, then guides participants through BRAPH 2 to build connectomes, compute graph measures, and run group comparisons, followed by a hands-on deep-learning pipeline. It demonstrates a unified GUI/command-line workflow, a unique architecture of BRAPH 2, helping participants move smoothly from the GUI to scripts. This workshop also guides participants to reproduce multiplex and deep-learning results on their computers from the BRAPH 2 bioRxiv preprint.
From images to graphs, this plenary shows how parcellations and tractography become connectomes and how network analysis reveals brain-network signatures. (Image by Y.-W. Chang.)Network analysis of neuroimaging data, and deep learning pipelines
Yu-Wei Chang NEMES 2025, 24-26 September, 2025 Date: 25 September 2025 Time: 09:00 – 09:45 Place: Clarion Hotel Draken
This plenary presents a practical framework for analysing neuroimaging data with network science and deep learning. It moves from modality-specific preprocessing to graph construction (single-layer and multiplex), then covers core graph measures, group inference, and brain-surface visualization, highlighting recent work from Associate Professor Joana B. Pereira’s group (Department of Clinical Neuroscience, Karolinska Institutet). It also introduces deep-learning pipelines for neuroimaging data: reservoir-computing memory capacity analysis, GapNet for handling missing data, and a robust feature-attribution method combined with SNP (single nucleotide polymorphism) information. The plenary concludes with the BRAPH 2 framework, which supports these pipelines and extends to other ongoing projects (e.g., light-sheet microscopy, Raman spectroscopy).
Alex Lech at the BNMI poster session. (Photo by M. Granfors)DeepTrack2: Microscopy Simulations for Deep Learning
Alex Lech, Mirja Granfors, Benjamin Midtvedt, Jesús Pineda, Harshith Bachimanchi, Carlo Manzo, Giovanni Volpe BNMI 2025, 19-22 August 2025, Gothenburg, Sweden Date: 20 August 2025 Time: 15:15-19:00 Place: Wallenberg Conference Centre
DeepTrack2 is a flexible and scalable Python library designed for simulating microscopy data to generate high-quality synthetic datasets for training deep learning models. It supports a wide range of imaging modalities, including brightfield, fluorescence, darkfield, and holography, allowing users to simulate realistic experimental conditions with ease. Its modular architecture enables users to customize experimental setups, simulate a variety of objects, and incorporate optical aberrations, realistic experimental noise, and other user-defined effects, making it suitable for various research applications. DeepTrack2 is designed to be an accessible tool for researchers in fields that utilize image analysis and deep learning, as it removes the need for labor-intensive manual annotation through simulations. This helps accelerate the development of AI-driven methods for experiments by providing large volumes of data that is often required by deep learning models. DeepTrack2 has already been used for a number of applications in cell tracking, classifications tasks, segmentations and holographic reconstruction. Its flexible and scalable nature enables researchers to simulate a wide array of experimental conditions and scenarios with full control of features and parameters.
DeepTrack2 is available on GitHub, with extensive documentation, tutorials, and an active community for support and collaboration at https://github.com/DeepTrackAI/DeepTrack2.
Quantitative Digital Microscopy with Deep Learning.
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt & Giovanni Volpe.
Applied Physics Reviews, volume 8, article number 011310 (2021).
DeepTrack2 Logo. (Image by J. Pineda)DeepTrack2: physics-based microscopy simulations for deep learning
Mirja Granfors, Alex Lech, Benjamin Midtvedt, Jesús Pineda, Harshith Bachimanchi, Carlo Manzo, and Giovanni Volpe BNMI 2025, 19-22 August 2025, Gothenburg, Sweden Date: 20 August 2025 Time: 15:00 – 15:15 Place: Wallenberg Conference Centre
DeepTrack2 is a flexible and scalable Python library designed to generate physics-based synthetic microscopy datasets for training deep learning models. It supports a wide range of imaging modalities, including brightfield, fluorescence, darkfield, and holography, enabling the creation of synthetic samples that accurately replicate real experimental conditions. Its modular architecture empowers users to customize optical systems, incorporate optical aberrations and noise, simulate diverse objects across various imaging scenarios, and apply image augmentations. DeepTrack2 is accompanied by a dedicated GitHub page, providing extensive documentation, examples, and an active community for support and collaboration: https://github.com/DeepTrackAI/DeepTrack2.
