arXiv subjects

Muhittin Mungan

Publications and source records attributed to Muhittin Mungan.

At least 19 recordsLinked to original sources

arXiv2025

Self-organization, Memory and Learning: From Driven Disordered Systems to Living Matter

Disordered systems subject to a fluctuating environment can self-organize into a complex history-dependent response, retaining a memory of the driving. In sheared amorphous solids, self-organization is established by the emergence of a persistent system of mechanical instabilities that can repeatedly be triggered by the driving, leading to a state of high mechanical reversibility. As a result of self-organization, the response of the system becomes correlated with the dynamics of its environment, which can be viewed as a sensing mechanism of the system's environment. Such phenomena emerge across a wide variety of soft matter systems, suggesting that they are generic and hence may depend very little on the underlying specifics. We review self-organization in driven amorphous solids, concluding with a discussion of what self-organization in driven disordered systems can teach us about how simple organisms sense and adapt to their changing environments.

cond-mat.soft↗

arXiv2025

Memory behavior of a randomly driven model glass

We investigate by atomistic simulations the memory behavior a model glass subjected to random driving protocols. The training consists of a random walk of forward and/or backward shearing sequences bounded by a maximal shear strain of absolute value γT . We show that such a stochastic training protocol is able to record the training amplitude. Different read-out protocols are also tested and are shown to be able to retrieve the training amplitude. We then emphasize the ten- sorial character of the memory encoded in the glass sample and then characterize the anisotropic mechanical behavior of the trained samples.

cond-mat.soft↗

arXiv2025

Coarse grained descriptions of the dynamics of yielding of amorphous solids under cyclic shear

Recent computer simulations reveal several intriguing features in the evolution of properties of amorphous solids subjected to repeated cyclic shear deformation. These include the divergence of the number of cycles to reach steady states as the yielding point is approached, a non-monotonic change of properties with cycles, and the possibility of a spectrum of frozen states. Theoretical attempts to capture these properties through simple models, including the Ehrenfest model describing a random walk in a confining potential, have met partial success. Here, we show that incorporating the influence of mechanical noise through a feedback term leads to a genuine dynamical transition with characteristics reflecting those of yielding. Coarse graining the dynamics into a small number of variables leads to new insights regarding the dynamics of yielding.

cond-mat.stat-mech↗

arXiv2025

Self-organization and memory in an disordered solid subject to random loading

We consider self-organization and memory formation in a mesoscopic model of an amorphous solid subject to a random shear strain protocol confined to a strain range $\pm \varepsilon_{\rm max}$. We develop proper read-out protocols to show that the response of the driven system retains a memory of the strain range, which can be subsequently retrieved. Our findings generalize previous results obtained upon oscillatory driving and suggest that self-organization and memory formation of disordered materials can emerge under more general conditions, such as a disordered system interacting with its fluctuating environment. The self-organization results in a correlation between the dynamics of the system and its environment. We conclude by discussing our results within the context of environmental sensing, highlighting their generalizability to adaptation strategies of simple organisms under changing conditions.

cond-mat.soft↗

arXiv2024

Self-organization and memory in a cyclically driven elasto-plastic model of an amorphous solid

The mechanical behavior of disordered materials such as dense suspensions, glasses or granular materials depends on their thermal and mechanical past. Here we report the memory behavior of a quenched mesoscopic elasto-plastic (QMEP) model. After prior oscillatory training, a simple read-out protocol gives access to both the training protocol's amplitude and the last shear direction. The memory of direction emerges from the development of a mechanical polarization during training. The analysis of sample-to-sample fluctuations gives direct access to the irreversibility transition. Despite the quadrupolar nature of the elastic interactions in amorphous solids, a behavior close to Return Point Memory (RPM) is observed. The quasi RPM property is used to build a simple Preisach-like model of directional memory.

cond-mat.soft↗

arXiv2022

A Driven Disordered Systems Approach to Biological Evolution in Changing Environments

Biological evolution of a population is governed by the fitness landscape, which is a map from genotype to fitness. However, a fitness landscape depends on the organisms environment, and evolution in changing environments is still poorly understood. We study a particular model of antibiotic resistance evolution in bacteria where the antibiotic concentration is an environmental parameter and the fitness landscapes incorporate tradeoffs between adaptation to low and high antibiotic concentration. With evolutionary dynamics that follow fitness gradients, the evolution of the system under slowly changing antibiotic concentration resembles the athermal dynamics of disordered physical systems under external drives. Exploiting this resemblance, we show that our model can be described as a system with interacting hysteretic elements. As in the case of the driven disordered systems, adaptive evolution under antibiotic concentration cycling is found to exhibit hysteresis loops and memory formation. We derive a number of analytical results for quasistatic concentration changes. We also perform numerical simulations to study how these effects are modified under driving protocols in which the concentration is changed in discrete steps. Our approach provides a general framework for studying motifs of evolutionary dynamics in biological systems in a changing environment.

q-bio.PE↗

arXiv2022

Mapping out the glassy landscape of a mesoscopic elastoplastic model

We develop a mesoscopic model to study the plastic behavior of an amorphous material under cyclic loading. The model is depinning-like and driven by a disordered thresholds dynamics which are coupled by long-range elastic interactions. We propose a simple protocol of "glass preparation" which allows us to mimic thermalisation at high temperature, as well as aging at vanishing temperature. Various levels of glass stabilities (from brittle to ductile) can be achieved by tuning the aging duration. The aged glasses are then immersed into a quenched disorder landscape and serve as initial configurations for various protocols of mechanical loading by shearing. The dependence of the plastic behavior upon monotonous loading is recovered. The behavior under cyclic loading is studied for different ages and system sizes. The size and age dependence of the irreversibility transition is discussed. A thorough characterization of the disorder-landscape is achieved through the analysis of the transition graphs, which describe the plastic deformation pathways under athermal quasi-static shear. In particular, the analysis of the stability ranges of the strongly connected components of the transition graphs reveals the emergence of a phase-separation like process associated with the aging of the glass. Increasing the age and hence stability of the initial glass, results in a gradual break-up of the landscape of dynamically accessible stable states into three distinct regions: one region centered around the initially prepared glass phase, and two additional regions, characterized by well-separated ranges of positive and negative plastic strains, each of which is accessible only from the initial glass phase by passing through the stress peak in the forward, respectively, backward shearing directions.

cond-mat.soft↗

arXiv2022

Yielding behavior of glasses under asymmetric cyclic deformation

We consider the yielding behaviour of a model glass subjected to asymmetric cyclic shear deformation, wherein the applied strain varies between 0 and a maximum value $γ_{\rm max}$, and study its dependence on the degree of annealing of the glass and system size. The yielding behaviour of well annealed glasses (unlike poorly annealed glasses) display striking differences from the symmetric case, with the emergence of an intermediate strain regime with substantial plasticity but no yielding. The observed behaviour is satisfactorily captured by a recently proposed model. For larger system sizes, the intermediate strain regime narrows, leading to a remarkable reversal of yield strain with annealing.

cond-mat.soft↗

arXiv2021

Metastability as a mechanism for yielding in amorphous solids under cyclic shear

We consider the yielding behavior of amorphous solids under cyclic shear deformation and show that it can be mapped into a random walk in a confining potential with an absorbing boundary. The resulting dynamics is governed by the first passage time into the absorbing state and suffices to capture the essential qualitative features recently observed in atomistic simulations of amorphous solids. Our results provide insight into the mechanism underlying yielding and its robustness. When the possibility of activated escape from absorbing states is added, it leads to a unique determination of a threshold energy and yield strain, suggesting thereby an appealing approach to understanding fatigue failure.

cond-mat.soft↗

arXiv2021

Cooperative effects driving the multi-periodic dynamics of cyclically sheared amorphous solids

Plasticity in amorphous materials, such as glasses, colloids, or granular materials, is mediated by local rearrangements called "soft spots". Experiments and simulations have shown that soft spots are two-state entities interacting via quadrupolar displacement fields generated when they switch states. When the system is subjected to cyclic strain driving, the soft spots can return to their original state after one or more forcing cycles. In this case, the system has periodic dynamics and will always repeat the same microscopic states. Here we focus on multi-periodic dynamics, i.e. dynamics that has periodicity larger than the periodicity of the drive, and use a graph-theoretical approach to analyze the dynamics obtained from numerical simulations. In this approach, mechanically stable configurations that transform purely elastically into each other over a range of applied strains, are represented by vertices, and plastic events leading from one stable configuration to the other, are represented by directed edges. An algorithm based on the graph topology and the displacement fields of the soft spots reveals that multi-periodic behavior results from the states of some soft spots repeating after more than one period and provides information regarding the mechanisms that allow for such dynamics. To better understand the physical mechanisms behind multi-periodicity, we use a model of interacting hysterons. Each hysteron is a simplified two-state element representing hysteretic soft-spot dynamics. We identify several mechanisms for multi-periodicity in this model, some involving direct interactions between multi-periodic hysterons and another resulting from cooperative dynamics involving several hysterons. These cooperative events are naturally more common when more hysterons are present, thus explaining why multi-periodicity is more prevalent at large drive amplitudes.

cond-mat.soft↗

arXiv2021

The delicate memory structure of origami switches

While memory effects emerge from systems of wildly varying length- and time-scales, the reduction of a complex system with many interacting elements into one simple enough to be understood without also losing the complex behavior continues to be a challenge. Here, we investigate how bistable cylindrical origamis provide such a reduction via tunably-interactive memory behaviors. We base our investigation on folded sheets of Kresling patterns that function as two-state memory units. By linking several units, each with a selected activation energy, we construct a one-dimensional material that exhibits return-point memory. After a comprehensive experimental analysis of the relation between the geometry of the pattern and the mechanical response for a single bit, we study the memory of a bellows composed of 4 bits arranged in series. Since these bits are decoupled, the system reduces to the Preisach model and we can drive the bellows to any of its 16 allowable states by following a prescribed sequence of compression and extension. We show how to reasonably discriminate between states by measuring the system's total height and stiffness near equilibrium. Furthermore, we establish the existence of geometrically-disallowed defective stable configurations which expand the configuration space to 64 states with a more complex transition pattern. Using empirical considerations of the mechanics, we analyze the hierarchical structure of the corresponding diagram, which includes Garden of Eden states and subgraphs. We highlight two irreversible transformations, shifting and erasure of the defect, leading to memory behaviors reminiscent of those observed with more complex glassy systems.

cond-mat.soft↗

arXiv2021

The Preisach graph and longest increasing subsequences

The Preisach graph is a directed graph associated with a permutation $ρ\in{\cal S}_N$. We give an explicit bijection between its vertices and increasing subsequences of $ρ$ with the property that the length of a subsequence equals to the degree of nesting of the corresponding vertex inside a hierarchy of cycles and sub-cycles of the graph. As a consequence, the nesting degree of the Preisach graph equals the length of the longest increasing subsequence.

math.PR↗

arXiv2021

Topology of the energy landscape of sheared amorphous solids and the irreversibility transition

Recent experiments and simulations of amorphous solids plastically deformed by oscillatory drive have foundsurprising behavior - for small strain amplitudes the dynamics can be reversible, which is contrary to the usual notion of plasticity as an irreversible form of deformation. This reversibility allows the system to reach limit-cycles in which plastic events repeat indefinitely under the oscillatory drive. Reaching reversible limit-cycles, can take a large number of driving cycles and it was surmised that the plastic events encountered during the transient period are not encountered again and are thus irreversible. Using a graph representation of the stable configurations of the system and the plastic events connecting them, we show that the notion of reversibility is more subtle. We find that reversible plastic events are abundant, and that a large portion of the plastic events encountered during the transient period are actually reversible, in the sense that they can be part of a reversible deformation path. We observe that the transition graph can be decomposed into clusters of configurations that are connected by reversible transitions. These clusters are the strongly connected components of the graph and their sizes turn out to be power-law distributed. The largest of these are grouped in regions of reversibility, which in turn are confined by regions of irreversibility whose number proliferates at larger strains. Our results provide an explanation for the irreversibility transition - the divergence of the transient period at a critical forcing amplitude. Long transients result from transition between clusters of reversibility in a search for a cluster large enough to contain a limit-cycle of a specific amplitude. For large enough amplitudes, the search time becomes very large, since sizes of the limit cycles become incompatible with the sizes of the regions of reversibility.

cond-mat.soft↗

arXiv2020

State transition graph of the Preisach model and the role of return point memory

The Preisach model has been useful as a null-model for understanding memory formation in periodically driven disordered systems. In amorphous solids for example, the athermal response to shear is due to localized plastic events (soft spots). As shown recently by one of us, the plastic response to applied shear can be rigorously described in terms of a directed network whose transitions correspond to one or more soft spots changing states. The topology of this graph depends on the interactions between soft-spots and when such interactions are negligible, the resulting description becomes that of the Preisach model. A first step in linking transition graph topology with the underlying soft-spot interactions is therefore to determine the structure of such graphs in the absence of interactions. Here we perform a detailed analysis of the transition graph of the Preisach model. We highlight the important role played by return point memory in organizing the graph into a hierarchy of loops and sub-loops. Our analysis reveals that the topology of a large portion of this graph is actually not governed by the values of the switching fields that describe the individual hysteretic behavior of the individual elements, but by a coarser parameter, a permutation $ρ$ which prescribes the sequence in which the individual hysteretic elements change their states as the main hysteresis loop is traversed. This in turn allows us to derive combinatorial properties, such as the number of major loops in the transition graph as well as the number of states $| \mathcal{R} |$ constituting the main hysteresis loop and its nested subloops. We find that $| \mathcal{R} |$ is equal to the number of increasing subsequences contained in the permutation $ρ$.

cond-mat.soft↗

arXiv2019

Networks and Hierarchies: How Amorphous Materials Learn to Remember

We consider the slow and athermal deformations of amorphous solids and show how the ensuing sequence of discrete plastic rearrangements can be mapped onto a directed network. The network topology reveals a set of highly connected regions joined by occasional one-way transitions. The highly connected regions include hierarchically organized hysteresis cycles and sub-cycles. At small to moderate strains this organization leads to near-perfect return point memory. The transitions in the network can be traced back to localized particle rearrangements (soft-spots) that interact via Eshelby-type deformation fields. By linking topology to dynamics, the network representations provides new insights into the mechanisms that lead to reversible and irreversible behavior in amorphous solids.

cond-mat.soft↗

arXiv2019

The structure of state transition graphs in hysteresis models with return point memory: I. General Theory

We consider the athermal quasi-static dynamics (AQS) of disordered systems driven by a slowly varying external field. Our interest is in an automaton description (AQS-A) that represents the AQS dynamics in terms of the graph of state transitions triggered by the driving field. A particular feature of these systems is return point memory (RPM), a tendency for the system to return to the same microstate upon cycling the field. It is known that the existence of three conditions, (1) a partial order on the set of configuration; (2) a no-passing property; and (3) an adiabatic response to monotonous driving fields, implies RPM. When periodically driven, such systems settle into a cyclic response after a transient of at most one period. However conditions (1)-(3) are only sufficient but not necessary. In fact, we show that the AQS dynamics naturally provides a more selective partial order which, due to its connection to hysteresis loops, is a natural choice for establishing RPM. This enables us to consider AQS-A exhibiting RPM without necessarily possessing the no-passing property. We call such automata $\ell$AQS-A and work out the structure of their state transition graphs. Our central finding is that RPM constrains the {\em intra-loop} structure of hysteresis loops, namely its hierarchical organization into sub loops, but not the {\em inter-loop} structure. We prove that the topology of the intra-loop structure has a natural representation in terms of an ordered tree and that the corresponding state transition graph is planar. On the other hand, the RPM property does not significantly restrict inter-loop transitions. A system exhibiting RPM and subject to periodic forcing can thus undergo a large number of transient cycles before settling into a periodic response. Such systems can even exhibit subharmonic response.

cond-mat.dis-nn↗

arXiv2019

Cyclic annealing as an iterated random map

Disordered magnets, martensitic mixed crystals, and glassy solids can be irreversibly deformed by subjecting them to external deformation. The deformation produces a smooth, reversible response punctuated by abrupt relaxation "glitches". Under appropriate repeated forward and reverse deformation producing multiple glitches, a strict repetition of a single sequence of microscopic configurations often emerges. We exhibit these features by describing the evolution of the system configuration from glitch to glitch as a mapping of $\mathcal{N}$ states into one-another. A map $\mathbf{U}$ controls forward deformation; a second map $\mathbf{D}$ controls reverse deformation. Iteration of a given sequence of forward and reverse maps, e.g. $\mathbf{DDDDUUU}$ necessarily produces a convergence to a fixed cyclic repetition of states covering multiple glitches. The repetition may have a period of more than one strain cycle, as recently observed in simulations. Using numerical sampling, we characterize the convergence properties of four types of random maps implementing successive physical restrictions. The most restrictive is the much-studied Preisach model. These maps show only the most qualitative resemblance to annealing simulations. However, they suggest further properties needed for a realistic mapping scheme.

cond-mat.soft↗

arXiv2016

Depinning as a coagulation process

We consider a one-dimensional sandpile model which mimics an elastic string of particles driven through a strongly pinning periodic environment with phase disorder. The evolution towards depinning occurs by the triggering of avalanches in regions of activity which are at first isolated but later grow and merge. For large system sizes the dynamically critical behavior is dominated by the coagulation of these active regions. Our analysis of the evolution and numerical simulations show that the observed sizes of active regions is well-described by a Smoluchowski coagulation equation, allowing us to predict correlation lengths and avalanche sizes.

cond-mat.stat-mech↗