DIEP@UvA ongoing projects and associated researchers

DIEP@UvA has various ongoing projects within emergence and the DIEP@UvA priority areas and many associated researchers. New potential team members who would like to join an exciting interdisciplinary research centre (see current DIEP fellows) can have an idea of potential co-workers and research topics below.

DIEP

@UvA

 

Korteweg-de Vries Institute for Mathematics (KdVI)

There are several research topics at KdVI that deal with emergent phenomena and align with the research priorities in the DIEP-UvA initiative.

 

One topic is the development and analysis of algorithms for multiscale modeling and simulation. At KdVI, Daan Crommelin works on stochastic and data-based methods for modeling the effective interaction between microscopic and macroscopic degrees of freedom, in the context of multiscale dynamical systems that are typically out-of-equilibrium. The uncertainties of this interaction can be represented with stochastic models. Determining suitable functional forms for these models is difficult and there is need for developing data-based methods (including machine learning) to tackle this.

The second topic is rare event simulation, concerning techniques for efficient simulation of e.g. rare transitions in metastable systems or extreme events in stochastic models. Two main classes of techniques are importance sampling and multilevel splitting. At KdVI, these techniques are designed and analysed, aimed at applications in e.g. queueing systems (such as communication networks) and climate science. Michel Mandjes and Daan Crommelin are involved in this research.

Another topic in the DIEP-UvA initiative is that of causality. At KdVI, the research of Joris Mooij is focused on causal inference, including foundations of causal modeling and estimation of causal models from data. This work involves a combination of statistics, machine learning and modeling, and encompasses both foundational and algorithmic aspects of causal modeling and inference.
A fourth topic is information theory. At KdVI, Michael Walter and Maris Ozols work on quantum information theory. Classical information-theoretic notions (such as entropy) need to be adapted, and new ones developed, when going from the classical to the quantum domain. Superposition and entanglement are two of the key concepts used in quantum information theory.
At a more general level, analyzing and using mathematical models are central to the study of emergent phenomena. In the Analysis programme of KdVI, research includes dynamical systems as well as numerical analysis, where models in the form of differential equations (both PDEs and ODEs) are studied. Some specific topics are bifurcation theory, wavelets and inverse problems. Furthermore, stochastic models and their analysis are the terrain of the KdVI Stochastics programme, which covers probability theory, mathematical statistics and operations research.
The are ongoing collaborations with the remaining institutes. Daan Crommelin collaborates with Alfons Hoekstra from IvI in the EU-H2020 project VECMA, a project related to the multiscale modeling and simulation topic. He is also collaborating with Peter Bolhuis from HIMS on rare event simulationJoris Mooij has strong ties to IvI. Michael Walter is affiliated with KdVI as well as IoP and ILLC. As staff member of QuSoft he is well-connected to all three institutes.

Multiscale modelling and simulation, rare events

Rare event simulation

Machine learning, causal modelling

Quantum and classical Information theory

Quantum and classical Information theory

REFERENCES

N. Verheul, D. Crommelin: Data-driven stochastic representations of unresolved features in multiscale models, Comm. Math. Sci, Vol. 14 (2016), pp 1213 – 1236

D.T. Crommelin, E. Vanden-Eijnden: Subgrid scale parameterization with conditional Markov chains, J. Atmos. Sci. (2008), Vol. 65, pages 2661-2675

 Weinan E.: Principles of multiscale modeling. Cambridge University Press (2011).

Pathak, J., Wikner, A., Fussell, R., Chandra, S., Hunt, B. R., Girvan, M., & Ott, E.: Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model. Chaos (2018), Vol. 28, 041101.

L’Ecuyer, P., Mandjes, M., & Tuffin, B. (2009): Importance sampling and rare event simulation. In: Rare event simulation using Monte Carlo methods, 17-38.

Mandjes, M. (2007): Large deviations for Gaussian queues. Wiley, New York.

Bisewski, K., Crommelin, D., & Mandjes, M. (2019): Rare event simulation for steady-state probabilities via recurrency cycles. Chaos, Vol. 29, 033131.

Rubino, G. & Tuffin, B. (eds.) (2009): Rare Event Simulation using Monte Carlo Methods. John Wiley & Sons.

Bucklew, J. (2004): Introduction to rare event simulation. Springer.

Mooij, J. M., Peters, J., Janzing, D., Zscheischler, J., & Schölkopf, B. (2016): Distinguishing cause from effect using observational data: methods and benchmarks. Journal of Machine Learning Research, 17(1), 1103-1204.

Mooij, J. M., Magliacane, S., Claassen, T. (2016): Joint Causal Inference from Multiple Contexts. arXiv:1611.10351v4; to appear in Journal of Machine Learning Research, 2020

Magliacane, S., Van Ommen, T., Claassen, T., Bongers, S., Versteeg, P., Mooij, J. M. (2018): Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions. Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

Peters, J., Janzing, D., & Schölkopf, B. (2017): Elements of causal inference: foundations and learning algorithms. MIT press.

 

Van 't Hoff Institute for Molecular Sciences (HIMS)

Within the computational chemistry there are several research directions that involve emergent phenomena and other DIEP@UvA priority areas. This type of research often starts with a microscopic model and investigates collective properties emerging on higher length and time scales. Typically, this involves multiscale modeling techniques. In particular, Peter Bolhuis focuses on multiscale modelling and simulation of complex molecular systems; creating understanding of emergent phenomena such as soft matter self-assembly, (nano)material properties, protein folding/aggregation, and self-organisation in active matter; a particularly important quest for the group is to find collective variables and apply dynamical coarse graining for complex rare event processes, in general and in specific applications. Tristan Bereau focuses on advanced (dynamical) coarse graining methodology; machine learning techniques applied to coarse-graining; stochastic thermodynamics to constrain dynamical path ensembles in the steady state. Bernd Ensing focuses on finding pathways in high dimensions and Jocelyne Vreede focuses on understanding complex biomolecular processes. All of these topics provide ample room for projects involving emergent behaviour in microscopic models of matter in and out of equilibrium.  

Joost Reek and Bas de Bruin lead the homogeneous catalysis group.  A current trend within the field of catalysis, a key technology for sustainability, involves moving from simple systems to more complex ones. For example, photo-redox catalysis, tandem catalysis, solar fuel catalysis and the combinations of electrocatalysis with chemocatalysis, all contain multiple components. A similar complexity is seen in multiple-site catalysts and in the combinations of selective reaction+separation+sensing systems. By moving to these more complex systems, feedback loops and kinetics of multiple reactions can lead to certain emergent complex behaviour. While this has been recognised, this has not yet been pursued in full depth. The group is in a good position to initiate a research program on complexity in catalysis, with the aim of laying the fundamentals for future applications in this area. ​Another research direction dealing with emergent properties, is the preparation of Dye Sensititsed solar cells (DSSC). If you put the components of such a device together, the resulting properties are more than the linear combination. It would be interesting to extend DSSC to catalytic versions where photocatalytic (Uphill) conversions are performed.

A related topic deals with systems that are out of equilibrium which require energy inputs. There is a growing interest in developing catalyst systems that can generate the required energy inputs for a variety of out of equilibrium systems. In addition, these topics can connect to other research institutes such as biology and informatics, via the HIMS initiative on computer-aided chemistry.

There are several ongoing relevant interdisciplinary collaborations, in particular Peter Bolhuis collaborates with IoP on self-organisation in non-equilibrium driven colloidal systems (with Peter Schall). He has exploratory collaborations with KdVI on collective variables for rare events (with Daan Crommelin), and with IvI on emergence of the causality from microscopic reversible processes using information theory (with Rick Quax).

Multiscale modelling and simulation, rare events, information theory

Coarse graining, machine learning, stochastic thermodynamics

Computational chemistry, finding lowest free energy pathways

Computation chemistry, modelling structure formation

Catalysis, non-equilibrium driven systems

Catalysis, non-equilibrium driven systems

REFERENCES
Bolhuis, P. G., Dellago, C., & Chandler, D. (2000): Reaction coordinates of biomolecular isomerization. Proceedings of the National Academy of Sciences, 97(11), 5877–5882. https://doi.org/10.1073/pnas.100127697

Lechner, W., Rogal, J., Juraszek, J., Ensing, B., & Bolhuis, P. G. (2010): Nonlinear reaction coordinate analysis in the reweighted path ensemble. The Journal of Chemical Physics, 133(17), 174110. https://doi.org/10.1063/1.3491818

Newton, A. C., Groenewold, J., Kegel, W. K., & Bolhuis, P. G. (2015): Rotational diffusion affects the dynamical self-assembly pathways of patchy particles. Proceedings of the National Academy of Sciences, 112(50), 15308–15313. https://doi.org/10.1073/pnas.1513210112

Ni, R., Cohen Stuart, M. A., & Bolhuis, P. G. (2015): Tunable Long Range Forces Mediated by Self-Propelled Colloidal Hard Spheres. Physical Review Letters, 114(1). https://doi.org/10.1103/physrevlett.114.018302

Evers, C. H. J., Luiken, J. A., Bolhuis, P. G., & Kegel, W. K. (2016): Self-assembly of microcapsules via colloidal bond hybridization and anisotropy. Nature, 534(7607), 364–368. https://doi.org/10.1038/nature17956

Arjun, Berendsen, T. A., & Bolhuis, P. G. (2019): Unbiased atomistic insight in the competing nucleation mechanisms of methane hydrates. Proceedings of the National Academy of Sciences, 116(39), 19305–19310. https://doi.org/10.1073/pnas.1906502116

Buijsman, P., & Bolhuis, P. G. (2020): Transition path sampling for non-equilibrium dynamics without predefined reaction coordinates. The Journal of Chemical Physics, 152(4), 44108. https://doi.org/10.1063/1.5130760

Bereau, T., & Rudzinski, J. F. (2018): Accurate structure-based coarse graining leads to consistent barrier-crossing dynamics. Physical review letters, 121(25), 256002. https://doi.org/10.1103/PhysRevLett.121.256002

Varolgunes, Y. B., Bereau, T., & Rudzinski, J. F. (2019): Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders. arXiv preprint arXiv:1912.12175. https://arxiv.org/abs/1912.12175

Hoffmann, ., Menichetti, R., Kanekal, K. H., & Bereau, T. (2019): Controlled exploration of chemical space by machine learning of coarse-grained representations. Physical Review E, 100(3), 033302. https://doi.org/10.1103/PhysRevE.100.033302

INFORMATICS INSTITUTE (IvI)

and INSTITUTE FOR ADVANCED STUDY (IAS)

There are multiple research projects at IvI and IAS that deal with emergent behaviour, network theory, information theory and causality; areas which are in line with DIEP@UvA priority areas
One such project consists of understanding social segregation as an emergent phenomenon. The issue of segregation in education has traditionally been examined from the individual level (e.g., parent surveys, choice analysis, etc.) as well as from macro-level statistics (e.g., changes in segregation level, region, city or national level). By using a novel complexity science approach, researchers are trying to connect these two levels, to understand how seemingly innocuous changes in individual behaviour or societal context can lead to drastic changes in macro level dynamics. The project deals with agent based models and complex systems. Researchers Mike Lees, Willem Boterman and Eric Dignum are involved in this project.
Another research direction consists in quantifying causality using information theoryComplex adaptive systems typically consist of multiple variables (nodes, agents, particles) which interact in a non-linear manner through a heterogeneous network of interactions, thereby generating a non-trivial systemic emergent behaviour which cannot be reduced to the dynamics of a single variable. The main idea behind information processing is that these variables can be interpreted as storing information (or memory); the interactions among variables can be seen as transmitting information from one variable to the other; and the decision of the new state of a variable based on all its interactions can be interpreted as integrating information (or information synergy). The original goal of this framework is to abstract away the mechanistic details of models (since regardless whether the variables represent neurons, birds, or molecules, the framework is solely in the language of ‘bits’) and thereby characterise emergent behaviours (e.g., tipping points, pattern formation, phase transitions) in a domain-free manner. Recently, though, the realisation is growing that this concept may be related to a notion of causality. That is, if a causal interaction makes information transmit from A to B, then in what way does this transmitted information represent causal influence?

 

A different research field altogether is that of causal discovery, causal inference. The most classic types of analyses performed in this field regards at least one of the following: static or equilibrium states, linear interactions, and/or ‘overwhelming’ interventions. Since we are interested in complex adaptive systems this project will focus instead on non-equilibrium dynamics, non-linear interactions, and ‘underwhelming/stochastic’ (nudge) interventions. This is an atypical setting in the causality inference field. Moreover and more importantly, the fields of information processing and causality inference currently hardly overlap nor interact with each other. This is a missed opportunity. Researchers Rick Quax and Peter Sloot are involved in this project.

Immune Fitness is a relatively new and exciting ongoing project, where one tries to understand the emergence of immunity as a coupled environmental/immune system co-evolution. Researchers Vivek Sheraton, Peter Sloot, Karien Stronks, Nadege Merabet, Ruud Brands are involved.
 
An interesting research direction deals with growth and form as an emergent pattern forming mechanism in multicellular organisms. In particular, one wishes to understand complex mechanisms of embryonic development and how they influence - and are influenced by - evolution. This is done by means of computational models of the mechanisms and evolution of animal body plan segmentation, and of evolutionary drift of embryonic development. This research direction is also closely connected with that of the Origins CenterWhat evolutionary driving forces could cause a transition to multicellularity, and how do crucial developmental mechanisms evolve in these early multicellular organisms? This are some of the questions that Renske Vroomans and Jaap Kaandorp want to address.
IvI has multiple interdisciplinary collaborations with other institutes. In particular Rick Quax has an ongoing collaboration with Peter Bolhuis (HIMS) on causality and information theory.

Complex systems, network theory, information theory

Complex systems, network theory, information theory

Computational biology, complex systems

REFERENCES

Rick Quax, Omri Har-Shemesh, Peter Sloot: Quantifying synergistic information using intermediate stochastic variables. Entropy 19.2 (2017): 85.

Rick Quax, et al.: Information processing features can detect behavioral regimes of dynamical systems. Complexity 2018.

Jakob Runge, et al., Rick Quax, et al.: Inferring causation from time series in Earth system sciences. Nature communications 10.1 (2019): 1-13.

Quax Rick, Apolloni Andrea and Sloot Peter M. A.: The diminishing role of hubs in dynamical processes on complex networks, 10, J. R. Soc. Interface

Presbitero, Alva, et al.: "Immune system model calibration by genetic algorithm." Procedia Computer Science 101 (2016): 161-171.

Presbitero, Alva, et al.: "Supplemented alkaline phosphatase supports the immune response in patients undergoing cardiac surgery: clinical and computational evidence." Frontiers in immunology 9 (2018): 2342.

Mancini, Emiliano, et al.: "HIV reservoirs and immune surveillance evasion cause the failure of structured treatment interruptions: A computational study." PloS one 7.4 (2012).

Mancini, Emiliano, et al.: "A study on the dynamics of temporary HIV treatment to assess the controversial outcomes of clinical trials: An in-silico approach." PloS one 13.7 (2018)

Roy, Debraj, et al.: "Spatial segregation, inequality, and opportunity bias in the slums of Bengaluru." Cities 74 (2018): 269-276.

Lutz, Wolfgang, et al.: "Demography's role in sustainable development." Science 335.6071 (2012): 918-918.

 

INSTITUTE FOR LOGIC, LANGUAGE AND COMPUTATION (ILLC)

The Institute for Logic, Language and Computation (ILLC) is an interdisciplinary research institute, shared between the Faculty of Science and the Faculty of Humanities, where logicians, mathematicians, computer scientists, cognitive scientists, linguists and philosophers collaborate. ILLC offers an international research environment with world-class faculty in all of its areas of specialisation, including several areas of specialisation within AI.

 

Several groups at ILLC are engaged in research that connects to emergent phenomena and the research themes emphasised in the DIEP-UvA initiative.

 

One large research domain covers collective behavior and emergent group intelligence and group decision making, using a variety of approaches. The ILLC research directions that are the most relevant to this topic are:

 

  • Logics for interactive rationality, pursued by Johan van Benthem, Sonja Smets, Alexandru Baltag and their Amsterdam Dynamics Group. This research studies multi-agent communication, strategic interaction, collective intelligence and its distortions (information cascades, etc),  combining techniques from modal logic (in particular dynamic  epistemic logics), belief revision theory (and belief merge), formal epistemology, topology, computational learning theory, social network theory and game theory.

 

  • Computational social choice, pursued by Ulle Endriss, Ronald de Haan, and their collaborators, looks at topics traditionally studied in social choice theory, preference aggregation, decision theory and game theory, using techniques and results from Theoretical Computer Science, in particular algorithms, complexity and logical specifications.

 

  • Cognition, its relevance for communication, music and other forms of interactive group-coordination, and its connections to computational-logical complexity. In the Cognition, Language and Computation lab, Jelle Zuidema and his group focus on the computational principles underlying natural language understanding and investigate the neural implementation of these principles in the human brain, their evolutionary origins and their usefulness in language technology. Jakub Szymanik and his group use formal models and cognitive representations to answer questions in linguistics and cognitive science.

Another research area is the study of causality and counterfactuals from a logical, epistemological and natural language perspective, pursued by Robert van Rooij, Katrin Schulz and their group. This approach combines philosophical ideas and probabilistic methods from Bayesian epistemology with logical methods used in the study of semantics and pragmatics of natural language.  More generally, this team runs the Causal Inference Lab and focusses on  the role causality plays in the interpretation of natural language, reasoning and decision making.

 

Yet another area of active research is in quantum information theory, quantum computation and quantum logic. The study of logics designed for capturing quantum information, entanglement, and various quantum protocols, is pursued by Sonja Smets, Alexandru Baltag and their group, who developed an approach known as quantum dynamic logic, extending with various qualitative and probabilistic features, and applied to the verification of quantum programs.  Further work on quantum information theory, when connected to investigations in quantum algorithms, quantum communication, quantum cryptography and quantum complexity theory, is being conducted within QuSoft (the Research Center for Quantum Software) in which ILLC is represented via Michael WalterMaris Ozols Harry Buhrman, Ronald de Wolf and Christian Schaffner.

Logic, information theory, game theory, collective intelligence

Logic, information theory, collective intelligence, epistemology

Social choice theory, game theory, complexity

Social choice theory, game theory, complexity

Logic, information theory, collective intelligence, epistemology

Logic, information theory, epistemology

Cognition, complexity, AI, logic, linguistics

Formal semantics, philosophical logic, metaphysics

Linguistics, philosophy, cognitive science and artificial intelligence

REFERENCES

A. Baltag and S. Smets. Complete Axiomatizations for Quantum Actions, in the proceedings of IQSA 2004, International Journal of Theoretical Physics 44(12): p.2267-2282, 2005.

A. Baltag, S. Smets and J. Zvesper. Keep ‘hoping’ for rationality: a solution to the backward induction paradox. In: Synthese. Volume 169, Number 2 / July, pp. 301-333, 2009.

A. Baltag and S. Smets. LQP: The Dynamic Logic of Quantum Information, in Mathematical Structures in Computer Science, Special Issue on Quantum Programming Languages, 16(3): p.491-525, 2006.

A. Baltag, Z. Christoff, J.U. Hansen and S. Smets. Logical Models of Informational Cascades. In J. van Benthem and F. Liu (eds.), Studies in Logic. College Publications, Volume 47, pp. 405-432, 2013.

S. Smets and F.R. Velazquez-Quesada. A logical perspective on social group creation. In The Logica Yearbook 2017, pp. 271-288, College Publications London, UK, 2018.

A. Baltag, Z. Christoff, R. Rendsvig, S. Smets. Dynamic Epistemic Logics of Diffusion and Prediction in Social Networks, Studia Logica, 1-43, online first, 2018.

S. Smets and F. R. Velázquez-Quesada. A logical study of group-size based social network creation. In Journal of Logical and Algebraic Methods in Programming, Volume 106, Pages 117-140, 2019.

A. Baltag and S. Smets, A Qualitative Theory of Dynamic Interactive Belief Revision, in G. Bonanno, W. van der Hoek, M. Wooldridge (eds.), Logic and the Foundations of Game and Decision Theory, Texts in Logic and Games, Vol 3, pp.9-58, Amsterdam University Press, 2008

Ulle Endriss. Collective Information. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-2020), February 2020. Blue Sky Ideas Track.

Weiwei Chen and Ulle Endriss. Preservation of Semantic Properties in Collective Argumentation: The Case of Aggregating Abstract Argumentation Frameworks. Artificial Intelligence, 269:27-48, 2019.

Ulle Endriss, editor. Trends in Computational Social Choice. AI Access, 2017.

Patricia Rich, Mark Blokpoel, Ronald de Haan, and Iris van Rooij, How Intractability Spans the Cognitive and Evolutionary Levels of Explanation. PsyArxiv Preprints, 2020.

Ronald de Haan, Parameterized Complexity in the Polynomial Hierarchy. Lecture Notes in Computer Science 11880, Springer, 2019.

Willem Zuidema, Robert M. French, Raquel G. Alhama, Kevin Ellis, Timothy J. O’Donnell, Tim Sainburg, Timothy Q. Gentner (2019), Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning. TopICS.

Dieuwke Hupkes, Sara Veldhoen & Willem Zuidema (2018), Visualisation and ‘diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure, In: Journal of Artificial Intelligence Research

J. van Benthem: Logical Dynamics of Information and Interaction, Cambridge University Press, 2011.

Fausto Carcassi, Shane Steinert-Threlkeld, and Jakub Szymanik. The emergence of monotone quantifiers via iterated learning. Proceedings of the 41st Annual Meeting of the Cognitive Science Society, 2019.

Iris van de Pol, Shane Steinert-Threlkeld, and Jakub Szymanik. Complexity and learnability in the explanation of semantic universals. Proceedings of the 41st Annual Meeting of the Cognitive Science Society, 2019.

van Rooij, R. A. M., & Schulz, K. (2019). A causal power theory of generics. Topoi.

van Rooij, R., & Schulz, K. (2019). Generic sentences: Representativeness or Causality? In M. T. Espinal, E. Castroviejo, M. Leonetti, L. McNally, & C. Real-Puigdollers (Eds.), Proceedings of Sinn und Bedeutung 23 (Vol. 2, pp. 409-425).

van Rooij, R., & Schulz, K. (2019). Conditionals, causality and conditional probability. Journal of Logic, Language and Information, 28(1), 55-71.

 

INSTITUTE OF PHYSICS (IoP)

The Institute of Physics works on several aspects of emergence, from high-energy to quantum and soft matter. Many of these are aligned with the research themes in the DIEP-UvA initiative.

 

One such direction deals with new phases of matter in quantum systems and in classical metamaterials. Concepts of topology from mathematics into the physics of condensed matter provides a highly efficient classification tool for classifying many of these phases, in fact in some cases (ordered states) it encompasses all known phases of matter. However, unlike emergent phases in correlated electron systems in the case of quantum matter, the emergence of topological phases is non-symmetry breaking, and involves the development of long-range quantum entanglement, which stabilises the topological order over the whole system. This is a new field and is moving fast, throwing up many as yet unanswered questions about the nature and properties of topological phases. Mark Golden, Anne de Visser and Erik van Heumen work actively in these directions.

There are also mechanical analogues of quantum topological phases, which blur the distinction between material properties and machine behaviour. These systems open up exciting possibilities for novel tools, devices and designer materials, at the same time as offering access to non-Hermitian topological matter, a subject very much in its infancy world-wide and pursued by Corentin Coulais and Jasper van Wezel. It is of strong interest to classify the physics emerging in non-Hermitian topological metamaterials. Here tools for addressing out of equilibrium systems are also relevant. 

Strongly-correlated electron systems also pose a theoretical challenge since Fermi-liquid theory does not apply. Proposals for understanding transport properties of such systems include holographic dualities such as the AdS/CFT correspondence, where classical gravity provides a good approximation to the quantum matter, and hydrodynamics, where the governing dynamics of electron flows is determined by the symmetries of the underlying microscopic theory and intertwined patterns of symmetry breaking. Experimental and theoretical efforts are pursued by Mark Golden, Erik van Heumen and Jácome Armas.

An important area of research is related to the very nature of spacetime and gravity, pursued by Jan de Boer and Erik Verlinde. Can we demonstrate that spacetime and gravity are emergent phenomena, which originate from the collective behaviour of underlying quantum degrees of freedom? There is growing evidence that the answer is yes, making spacetime and gravity key examples of emergent behaviour. While this has not been understood in full generality, the AdS/CFT correspondence has established for the first time a precise quantitative equivalence between a quantum system and a particular classical gravitating spacetime. Although at first sight this correspondence appears to be rather specific, it is now widely believed that it is an instance of a very general and important universality class of emergent phenomena. There are indications that several strongly coupled systems in nature, which so far defied a proper understanding, may belong to this particular universality class. This line of research has led to new perspectives and even a new language for describing these emergent phenomena. This new language has been formulated using general techniques, for example the renormalisation group is connected to the emergence of a given geometric dimension of spacetime and thermalisation translates into black hole formation, but recently it has become clear that notions such as quantum entanglement and (quantum) information theory are instrumental in understanding technical aspects of the emergence of spacetime. 

Another direction of research concerns understanding the origins of emergence in the history of science that partly aims at deepening our understanding of the meaning of emergence by studying specific cases of emergent behaviour and what can be learnt from it. This line of research is pursued by Jeroen van Dongen

Quantum matter, strongly correlated systems

Quantum matter, strongly correlated systems

Quantum matter, strongly correlated systems

Metamaterials, soft matter

Quantum matter, matematerials

Quantum gravity, string theory, quantum information

Emergent gravity, string theory, quantum information

String theory, hydrodynamics

History of science and physics

REFERENCES

Ananya GhatakMartin BrandenbourgerJasper van WezelCorentin Coulais: Observation of non-Hermitian topology and its bulk-edge correspondence, arXiv:1907.11619.

Coulais C. et al. (2017), Static non-reciprocity in mechanical metamaterials, Nature 542, 461.

Verlinde E. (2017), Emergent gravity and the dark universe, SciPost Physics, 2, 016.

Yang, C., Cattelan, M., Fox, N., Huang, Y., Golden, M. S., & Schwarzacher, W. (2019). Electrochemical Modification and Characterization of Topological Insulator Single Crystals. Langmuir, 35(8), 2983-2988.

Bastiaans, K. M., Cho, D., Benschop, T., Battisti, I., Huang, Y., Golden, M. S., ... Allan, M. P. (2018). Charge trapping and super-Poissonian noise centres in a cuprate superconductor. Nature Physics, 14(12), 1183-1187

van Heumen, E., Berben, M., Neubrand, L., & Huang, Y. (2019). Scattering rate collapse driven by a van Hove singularity in the Dirac semimetal PdTe2. Physical Review Materials, 3(11), [114202].

Li, C., de Boer, J. C., de Ronde, B., Ramankutty, S. V., van Heumen, E., Huang, Y., ... Brinkman, A. (2018). 4π-periodic Andreev bound states in a Dirac semimetal. Nature Materials, 17(10), 875-880.

Bao, L., Yang, F., Tegus, O., Huang, Y. K., Leng, H. Q., & de Visser, A. (2019). Enhanced electron-phonon coupling in NbB2 by nanoscaling the grain size. Materials Characterization, 150, 13-21.

Xu, G., de Visser, A., Huang, Y., & Mao, X. (2019). Quantum Oscillations and Chiral Anomaly in a Bi0.96Sb0.04 Single Crystal. ADVANCES IN CONDENSED MATTER PHYSICS, 2019. 

J. Armas and A. Jain: Hydrodynamics for charge density waves and their holographic duals, arXiv:2001.07357

de Boer, J., van Breukelen, R., Lokhande, S. F., Papadodimas, K., & Verlinde, E. (2019). On the interior geometry of a typical black hole microstate. JHEP, 2019(5), [10].

Kruthoff, J., de Boer, J., van Wezel, J., Kane, C. L., & Slager, R. J. (2017). Topological classification of crystalline insulators through band structure combinatorics. Physical Review X, 7(4), [041069]. 

van Dongen, J., De Haro, S., Visser, M., & Butterfield, J. (2019): Emergence and correspondence for string theory black holes. Studies in History and Philosophy of Science Part B - Studies in History and Philosophy of Modern Physics. https://doi.org/10.1016/j.shpsb.2019.11.002

Dieks, D., van Dongen, J., & de Haro, S. (2015): Emergence in holographic scenarios for gravity. Studies in the History and Philosophy of Modern Physics, 52, 203-216. https://doi.org/10.1016/j.shpsb.2015.07.007 [details]

 

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