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2019


Controlling Heterogeneous Stochastic Growth Processes on Lattices with Limited Resources
Controlling Heterogeneous Stochastic Growth Processes on Lattices with Limited Resources

Haksar, R., Solowjow, F., Trimpe, S., Schwager, M.

In Proceedings of the 58th IEEE International Conference on Decision and Control (CDC) , pages: 1315-1322, 58th IEEE International Conference on Decision and Control (CDC), December 2019 (conference)

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PDF [BibTex]

2019


PDF [BibTex]


A Learnable Safety Measure
A Learnable Safety Measure

Heim, S., Rohr, A. V., Trimpe, S., Badri-Spröwitz, A.

Conference on Robot Learning, November 2019 (conference) Accepted

dlg ics

Arxiv [BibTex]

Arxiv [BibTex]


Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees
Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees

Baumann, D., Mager, F., Jacob, R., Thiele, L., Zimmerling, M., Trimpe, S.

ACM Transactions on Cyber-Physical Systems, 4(2):18, November 2019 (article)

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arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


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Sampling on Networks: Estimating Eigenvector Centrality on Incomplete Networks

Ruggeri, N., De Bacco, C.

International Conference on Complex Networks and Their Applications, November 2019 (article)

Abstract
We develop a new sampling method to estimate eigenvector centrality on incomplete networks. Our goalis to estimate this global centrality measure having at disposal a limited amount of data. This is the case inmany real-world scenarios where data collection is expensive, the network is too big for data storage capacityor only partial information is available. The sampling algorithm is theoretically grounded by results derivedfrom spectral approximation theory. We studied the problemon both synthetic and real data and tested theperformance comparing with traditional methods, such as random walk and uniform sampling. We show thatapproximations obtained from such methods are not always reliable and that our algorithm, while preservingcomputational scalability, improves performance under different error measures.

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Code Preprint pdf DOI [BibTex]

Code Preprint pdf DOI [BibTex]


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Dynamics of beneficial epidemics

Berdahl, A., Brelsford, C., De Bacco, C., Dumas, M., Ferdinand, V., Grochow, J. A., nt Hébert-Dufresne, L., Kallus, Y., Kempes, C. P., Kolchinsky, A., Larremore, D. B., Libby, E., Power, E. A., A., S. C., Tracey, B. D.

Scientific Reports, 9, pages: 15093, October 2019 (article)

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DOI [BibTex]

DOI [BibTex]


Predictive Triggering for Distributed Control of Resource Constrained Multi-agent Systems
Predictive Triggering for Distributed Control of Resource Constrained Multi-agent Systems

Mastrangelo, J. M., Baumann, D., Trimpe, S.

In Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems, pages: 79-84, 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys), September 2019 (inproceedings)

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arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


Event-triggered Pulse Control with Model Learning (if Necessary)
Event-triggered Pulse Control with Model Learning (if Necessary)

Baumann, D., Solowjow, F., Johansson, K. H., Trimpe, S.

In Proceedings of the American Control Conference, pages: 792-797, American Control Conference (ACC), July 2019 (inproceedings)

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arXiv PDF Project Page [BibTex]

arXiv PDF Project Page [BibTex]


Data-driven inference of passivity properties via Gaussian process optimization
Data-driven inference of passivity properties via Gaussian process optimization

Romer, A., Trimpe, S., Allgöwer, F.

In Proceedings of the European Control Conference, European Control Conference (ECC), June 2019 (inproceedings)

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PDF [BibTex]

PDF [BibTex]


Trajectory-Based Off-Policy Deep Reinforcement Learning
Trajectory-Based Off-Policy Deep Reinforcement Learning

Doerr, A., Volpp, M., Toussaint, M., Trimpe, S., Daniel, C.

In Proceedings of the International Conference on Machine Learning (ICML), International Conference on Machine Learning (ICML), June 2019 (inproceedings)

Abstract
Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks. However, these methods are also data-inefficient, afflicted with high variance gradient estimates, and frequently get stuck in local optima. This work addresses these weaknesses by combining recent improvements in the reuse of off-policy data and exploration in parameter space with deterministic behavioral policies. The resulting objective is amenable to standard neural network optimization strategies like stochastic gradient descent or stochastic gradient Hamiltonian Monte Carlo. Incorporation of previous rollouts via importance sampling greatly improves data-efficiency, whilst stochastic optimization schemes facilitate the escape from local optima. We evaluate the proposed approach on a series of continuous control benchmark tasks. The results show that the proposed algorithm is able to successfully and reliably learn solutions using fewer system interactions than standard policy gradient methods.

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arXiv PDF [BibTex]

arXiv PDF [BibTex]


Resource-aware IoT Control: Saving Communication through Predictive Triggering
Resource-aware IoT Control: Saving Communication through Predictive Triggering

Trimpe, S., Baumann, D.

IEEE Internet of Things Journal, 6(3):5013-5028, June 2019 (article)

Abstract
The Internet of Things (IoT) interconnects multiple physical devices in large-scale networks. When the 'things' coordinate decisions and act collectively on shared information, feedback is introduced between them. Multiple feedback loops are thus closed over a shared, general-purpose network. Traditional feedback control is unsuitable for design of IoT control because it relies on high-rate periodic communication and is ignorant of the shared network resource. Therefore, recent event-based estimation methods are applied herein for resource-aware IoT control allowing agents to decide online whether communication with other agents is needed, or not. While this can reduce network traffic significantly, a severe limitation of typical event-based approaches is the need for instantaneous triggering decisions that leave no time to reallocate freed resources (e.g., communication slots), which hence remain unused. To address this problem, novel predictive and self triggering protocols are proposed herein. From a unified Bayesian decision framework, two schemes are developed: self triggers that predict, at the current triggering instant, the next one; and predictive triggers that check at every time step, whether communication will be needed at a given prediction horizon. The suitability of these triggers for feedback control is demonstrated in hardware experiments on a cart-pole, and scalability is discussed with a multi-vehicle simulation.

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PDF arXiv DOI [BibTex]

PDF arXiv DOI [BibTex]


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Aging phenomena during phase separation in fluids: decay of autocorrelation for vapor-liquid transitions

Roy, S., Bera, A., Majumder, S., Das, S. K.

Soft Matter, 15(23):4743-4750, Royal Society of Chemistry, Cambridge, UK, May 2019 (article)

Abstract
We performed molecular dynamics simulations to study relaxation phenomena during vapor–liquid transitions in a single component Lennard-Jones system. Results from two different overall densities are presented: one in the neighborhood of the vapor branch of the coexistence curve and the other being close to the critical density. The nonequilibrium morphologies, growth mechanisms and growth laws in the two cases are vastly different. In the low density case growth occurs via diffusive coalescence of droplets in a disconnected morphology. On the other hand, the elongated structure in the higher density case grows via advective transport of particles inside the tube-like liquid domains. The objective in this work has been to identify how the decay of the order-parameter autocorrelation, an important quantity to understand aging dynamics, differs in the two cases. In the case of the disconnected morphology, we observe a very robust power-law decay, as a function of the ratio of the characteristic lengths at the observation time and at the age of the system, whereas the results for the percolating structure appear rather complex. To quantify the decay in the latter case, unlike the standard method followed in a previous study, here we have performed a finite-size scaling analysis. The outcome of this analysis shows the presence of a strong preasymptotic correction, while revealing that in this case also, albeit in the asymptotic limit, the decay follows a power-law. Even though the corresponding exponents in the two cases differ drastically, this study, combined with a few recent ones, suggests that power-law behavior of this correlation function is rather universal in coarsening dynamics.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Feedback Control Goes Wireless: Guaranteed Stability over Low-power Multi-hop Networks
Feedback Control Goes Wireless: Guaranteed Stability over Low-power Multi-hop Networks

(Best Paper Award)

Mager, F., Baumann, D., Jacob, R., Thiele, L., Trimpe, S., Zimmerling, M.

In Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, pages: 97-108, 10th ACM/IEEE International Conference on Cyber-Physical Systems, April 2019 (inproceedings)

Abstract
Closing feedback loops fast and over long distances is key to emerging applications; for example, robot motion control and swarm coordination require update intervals below 100 ms. Low-power wireless is preferred for its flexibility, low cost, and small form factor, especially if the devices support multi-hop communication. Thus far, however, closed-loop control over multi-hop low-power wireless has only been demonstrated for update intervals on the order of multiple seconds. This paper presents a wireless embedded system that tames imperfections impairing control performance such as jitter or packet loss, and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for linear dynamic systems. Using experiments on a testbed with multiple cart-pole systems, we are the first to demonstrate the feasibility and to assess the performance of closed-loop control and coordination over multi-hop low-power wireless for update intervals from 20 ms to 50 ms.

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arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems
Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems

Baumann, D.

KTH Royal Institute of Technology, Stockholm, Febuary 2019 (phdthesis)

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PDF [BibTex]

PDF [BibTex]


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Response of active Brownian particles to shear flow

Asheichyk, K., Solon, A., Rohwer, C. M., Krüger, M.

The Journal of Chemical Physics, 150(14), American Institute of Physics, Woodbury, N.Y., 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Vortex Mass in the Three-Dimensional O(2) Scalar Theory

Delfino, G., Selke, W., Squarcini, A.

Physical Review Letters, 122(5), American Physical Society, Woodbury, N.Y., 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Ferromagnetic colloids in liquid crystal solvents

Zarubin, G.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Fluctuating interface with a pinning potential

Pranjić, Daniel

Universität Stuttgart, Stuttgart, 2019 (mastersthesis)

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[BibTex]

[BibTex]


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Dynamics near planar walls for various model self-phoretic particles

Bayati, P., Popescu, M. N., Uspal, W. E., Dietrich, S., Najafi, A.

Soft Matter, 15(28):5644-5672, Royal Society of Chemistry, Cambridge, UK, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Glucose Oxidase Micropumps: Multi-Faceted Effects of Chemical Activity on Tracer Particles Near the Solid-Liquid Interface

Munteanu, R. E., Popescu, M. N., Gáspár, S.

Condensed Matter, 4(3), MDPI, Basel, 2019 (article)

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DOI [BibTex]


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Criticality senses topology

Vasilyev, O. A., Maciolek, A., Dietrich, S.

EPL, 128(2), EDP Science, Les-Ulis, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Soft Sensors for Curvature Estimation under Water in a Soft Robotic Fish

Wright, Brian, Vogt, Daniel M., Wood, Robert J., Jusufi, Ardian

In 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft 2019), pages: 367-371, IEEE, Piscataway, NJ, 2nd IEEE International Conference on Soft Robotics (RoboSoft 2019), 2019 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Drag Force for Asymmetrically Grafted Colloids in Polymer Solutions

Werner, M., Malgaretti, P., Maciolek, A.

Frontiers in Physics, 7, Frontiers Media, Lausanne, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Feeling Your Neighbors across the Walls: How Interpore Ionic Interactions Affect Capacitive Energy Storage

Kondrat, S., Vasilyev, O., Kornyshev, A. A.

The Journal of Physical Chemistry Letters, 10(16):4523-4527, American Chemical Society, Washington, DC, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Active Janus colloids at chemically structured surfaces

Uspal, W. E., Popescu, M. N., Dietrich, S., Tasinkevych, M.

The Journal of Chemical Physics, 150(20), American Institute of Physics, Woodbury, N.Y., 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Illumination-induced motion of a Janus nanoparticle in binary solvents

Araki, T., Maciolek, A.

Soft Matter, 15(26):5243-5254, Royal Society of Chemistry, Cambridge, UK, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Transient response of an electrolyte to a thermal quench

Janssen, M., Bier, M.

Physical Review E, 99(4), American Physical Society, Melville, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Controlling pattern formation in the confined Schnakenberg model

Beyer, David Bernhard

Universität Stuttgart, Stuttgart, 2019 (mastersthesis)

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[BibTex]

[BibTex]


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Flux and storage of energy in nonequilibrium stationary states

Holyst, R., Maciolek, A., Zhang, Y., Litniewski, M., Knycha\la, P., Kasprzak, M., Banaszak, M.

Physical Review E, 99(4), American Physical Society, Melville, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Correlations and forces in sheared fluids with or without quenching

Rohwer, C. M., Maciolek, A., Dietrich, S., Krüger, M.

New Journal of Physics, 21, IOP Publishing, Bristol, 2019 (article)

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DOI [BibTex]


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Ensemble dependence of critical Casimir forces in films with Dirichlet boundary conditions

Rohwer, C. M., Squarcini, A., Vasilyev, O., Dietrich, S., Gross, M.

Physical Review E, 99(6), American Physical Society, Melville, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Controlling the dynamics of colloidal particles by critical Casimir forces

Magazzù, A., Callegari, A., Staforelli, J. P., Gambassi, A., Dietrich, S., Volpe, G.

Soft Matter, 15(10):2152-2162, Royal Society of Chemistry, Cambridge, UK, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Charge regulation radically modifies electrostatics in membrane stacks

Majee, A., Bier, M., Blossey, R., Podgornik, R.

Physical Review E, 100(5), American Physical Society, Melville, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Co-Contraction facilitates Body Stiffness Modulation during Swimming with Sensory Feedback in a Soft Biorobotic Physical Model

Jusufi, A., Vogt, D., Wood, R. J.

Integrative and Comparative Biology, 59(Supplement 1):E116-E116, Society of Integrative and Comparative Biology, McLean, VA, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Comment on "Which interactions dominate in active colloids?" [J. Chem. Phys. 150, 061102 (2019)]

Popescu, M. N., Dominguez, A., Uspal, W. E., Tasinkevych, M., Dietrich, S.

The Journal of Chemical Physics, 151(6), American Institute of Physics, Woodbury, N.Y., 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Current-mediated synchronization of a pair of beating non-identical flagella

Dotsenko, V., Maciolek, A., Oshanin, G., Vasilyev, O., Dietrich, S.

New Journal of Physics, 21, IOP Publishing, Bristol, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Driving an electrolyte through a corrugated nanopore

Malgaretti, P., Janssen, M., Pagonabarraga, I., Rubi, J. M.

The Journal of Chemical Physics, 151(8), American Institute of Physics, Woodbury, N.Y., 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Interfaces in fluids of ionic liquid crystals

Bartsch, H.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Spectral Content of a Single Non-Brownian Trajectory

Krapf, D., Lukat, N., Marinari, E., Metzler, R., Oshanin, G., Selhuber-Unkel, C., Squarcini, A., Stadler, L., Weiss, M., Xu, X.

Physical Review X, 9(1), American Physical Society, New York, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Curvature affects electrolyte relaxation: Studies of spherical and cylindrical electrodes

Janssen, M.

Physical Review E, 100(4), American Physical Society, Melville, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Actively Learning Dynamical Systems with Gaussian Processes

Buisson-Fenet, M.

Mines ParisTech, PSL University, 2019 (mastersthesis)

Abstract
Predicting the behavior of complex systems is of great importance in many fields such as engineering, economics or meteorology. The evolution of such systems often follows a certain structure, which can be induced, for example from the laws of physics or of market forces. Mathematically, this structure is often captured by differential equations. The internal functional dependencies, however, are usually unknown. Hence, using machine learning approaches that recreate this structure directly from data is a promising alternative to designing physics-based models. In particular, for high dimensional systems with nonlinear effects, this can be a challenging task. Learning dynamical systems is different from the classical machine learning tasks, such as image processing, and necessitates different tools. Indeed, dynamical systems can be actuated, often by applying torques or voltages. Hence, the user has a power of decision over the system, and can drive it to certain states by going through the dynamics. Actuating this system generates data, from which a machine learning model of the dynamics can be trained. However, gathering informative data that is representative of the whole state space remains a challenging task. The question of active learning then becomes important: which control inputs should be chosen by the user so that the data generated during an experiment is informative, and enables efficient training of the dynamics model? In this context, Gaussian processes can be a useful framework for approximating system dynamics. Indeed, they perform well on small and medium sized data sets, as opposed to most other machine learning frameworks. This is particularly important considering data is often costly to generate and process, most of all when producing it involves actuating a complex physical system. Gaussian processes also yield a notion of uncertainty, which indicates how sure the model is about its predictions. In this work, we investigate in a principled way how to actively learn dynamical systems, by selecting control inputs that generate informative data. We model the system dynamics by a Gaussian process, and use information-theoretic criteria to identify control trajectories that maximize the information gain. Thus, the input space can be explored efficiently, leading to a data-efficient training of the model. We propose several methods, investigate their theoretical properties and compare them extensively in a numerical benchmark. The final method proves to be efficient at generating informative data. Thus, it yields the lowest prediction error with the same amount of samples on most benchmark systems. We propose several variants of this method, allowing the user to trade off computations with prediction accuracy, and show it is versatile enough to take additional objectives into account.

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[BibTex]

[BibTex]


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Dynamics of the critical Casimir force for a conserved order parameter after a critical quench

Gross, M., Rohwer, C. M., Dietrich, S.

Physical Review E, 100(1), American Physical Society, Melville, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Interface structures in ionic liquid crystals

Bartsch, H., Bier, M., Dietrich, S.

Soft Matter, 15(20):4109-4126, Royal Society of Chemistry, Cambridge, UK, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Interfacial premelting of ice in nano composite materials

Li, H., Bier, M., Mars, J., Weiss, H., Dippel, A., Gutowski, O., Honkimäki, V., Mezger, M.

Physical Chemistry Chemical Physics, 21(7):3734-3741, Royal Society of Chemistry, Cambridge, England, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Connections Matter: On the Importance of Pore Percolation for Nanoporous Supercapacitors

Vasilyev, O., Kornyshev, A. A., Kondrat, S.

ACS Applied Energy Materials, 2(8):5386-5390, American Chemical Society, Washington, DC, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Theory of light-activated catalytic Janus particles

Uspal, W. E.

The Journal of Chemical Physics, 150(11), American Institute of Physics, Woodbury, N.Y., 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Recovering superhydrophobicity in nanoscale and macroscale surface textures

Giacomello, A., Schimmele, L., Dietrich, S., Tasinkevych, M.

Soft Matter, 15(37):7462-7471, Royal Society of Chemistry, Cambridge, UK, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Brownian dynamics assessment of enhanced diffusion exhibited by "fluctuating-dumbbell enzymes".

Kondrat, S., Popescu, M. N.

Physical Chemistry Chemical Physics, 21(35):18811-18815, Royal Society of Chemistry, Cambridge, England, 2019 (article)

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DOI [BibTex]

DOI [BibTex]