arXiv subjects

Douglas J. Durian

Publications and source records attributed to Douglas J. Durian.

At least 19 recordsLinked to original sources

arXiv2026

Effective exponents for the diffusive coarsening of wet foams and analogous materials

It is a long-standing puzzle why experimentally measured exponents for power law growth of the average bubble radius cross over from 1/2 in the dry limit to 1/3 near the jamming transition, rather than in the very wet limit of dilute bubbles. Here, this is explained by calculation of the diffuse gas exchange rate between nearly kissing spherical bubbles. The result is only logarithmically faster than a 1/3 power law, a difference too small to be seen in existing data. The same approach is generalized to jammed foams, where bubbles are modeled as truncated spheres with circular soap films of average radii $fR$ where $R$ is the average bubble radius. The result is a non-power law growth law that gives an effective exponent that varies from 1/2 toward 1/3 as $f$ decreases to 0 at the jamming transition. This exponent prediction is compared with existing data sets in terms of a system-specific connection of $f$ to liquid content.

cond-mat.soft↗

arXiv2026

Understanding and Embracing Imperfection in Physical Learning Networks

Performing machine learning with analog signals offers advantages in speed and energy efficiency, but sensitivity to component and measurement imperfections often foils training without a system-specific companion digital model. Here we take a different perspective, accepting and characterizing these inherent imperfections and ultimately overcoming them without digital models. We train an analog network of self-adjusting resistors -- a contrastive local learning network -- for multiple tasks, and observe limit cycles and scaling behaviors that limit precision, erase memory of previous tasks, and are absent in `perfect' systems. We develop an analytical model capturing these phenomena as a consequence of an uncontrolled learning bias continuously modifying the underlying representation of learned tasks, reminiscent of representational drift in the brain. Finally, we introduce and demonstrate a system-agnostic training method that greatly suppresses these effects. Our work points to a new, scalable analog approach that eschews precise modeling and instead thrives in the mess of real systems.

cond-mat.dis-nn↗

arXiv2025

Collective Behavior and Memory States in Flow Networks with Tunable Bistability

Multistability-induced hysteresis has been widely studied in mechanical systems, but such behavior has proven more difficult to reproduce experimentally in flow networks. Natural flow networks like animal and plant vasculature can exhibit complex nonlinear behavior to facilitate fluid transport, so multistable flows may inform their functionality. To probe such phenomena in an analogous model system, we utilize an electronic network of hysteretic bistable resistors designed to have tunable negative differential resistivity. We demonstrate our system's capability to generate complex global memory states in the form of voltage patterns, which is mediated by the tunable nonlinearity of each element's current-voltage characteristic. We investigate avalanching behavior arising from effective interactions, and demonstrate how to encode explicit interactions of arbitrary form by taking advantage of the tunable circuitry design.

cond-mat.soft↗

arXiv2025

Solving the compute crisis with physics-based ASICs

Escalating artificial intelligence (AI) demands expose a critical "compute crisis" characterized by unsustainable energy consumption, prohibitive training costs, and the approaching limits of conventional CMOS scaling. Physics-based Application-Specific Integrated Circuits (ASICs) present a transformative paradigm by directly harnessing intrinsic physical dynamics for computation rather than expending resources to enforce idealized digital abstractions. By relaxing the constraints needed for traditional ASICs, like enforced statelessness, unidirectionality, determinism, and synchronization, these devices aim to operate as exact realizations of physical processes, offering substantial gains in energy efficiency and computational throughput. This approach enables novel co-design strategies, aligning algorithmic requirements with the inherent computational primitives of physical systems. Physics-based ASICs could accelerate critical AI applications like diffusion models, sampling, optimization, and neural network inference as well as traditional computational workloads like scientific simulation of materials and molecules. Ultimately, this vision points towards a future of heterogeneous, highly-specialized computing platforms capable of overcoming current scaling bottlenecks and unlocking new frontiers in computational power and efficiency.

cs.ET↗

arXiv2025

Stochastic dynamics of granular hopper flows: a configurational mode controls the stability of clogs

Granular flows in small-outlet hoppers exhibit several characteristic but poorly understood behaviors: temporary clogs (pauses) where flow stops before later spontaneously restarting, permanent clogs that last indefinitely, and non-Gaussian, non-monotonic flow-rate statistics. These aspects have been studied independently, but a model of hopper flow that includes all three has not been formulated. Here, we introduce a phenomenological model that provides a unifying dynamical explanation of all three behaviors: coupling between the flow rate and a hidden mode that controls the stability of clogs. In the theory, flow rate evolves according to Langevin dynamics with multiplicative noise and an absorbing state at zero flow, conditional on the hidden mode. The model fully reproduces the statistics of pause and clog events of a large ($>40,000$ flows) experimental dataset, including non-exponentially distributed clogging times and non-Gaussian flow rate distribution, and explains the stretched-exponential growth of the average clogging time with outlet size. Further, we identify the physical nature of the hidden mode in microscopic configurational features, including size and smoothness of the static arch structure formed during pauses and clogs. Our work provides a unifying framework for several poorly understood clogging phenomena, and suggests numerous new paths toward further understanding of this complex system.

cond-mat.soft↗

arXiv2024

Disorder Enhances the Fracture Toughness of Mechanical Metamaterials

Mechanical metamaterials with engineered failure properties typically rely on periodic unit cell geometries or bespoke microstructures to achieve their unique properties. We demonstrate that intelligent use of disorder in metamaterials leads to distributed damage during failure, resulting in enhanced fracture toughness with minimal losses of strength. Toughness depends on the level of disorder, not a specific geometry, and the confined lattices studied exhibit a maximum toughness enhancement at an optimal level of disorder. A mechanics model that relates disorder to toughness without knowledge of the crack path is presented. The model is verified through finite element simulations and experiments utilizing photoelasticity to visualize damage during failure. At the optimal level of disorder, the toughness is more than 2.6x of an ordered lattice of equivalent density.

cond-mat.mtrl-sci↗

arXiv2024

Experimental Demonstration of Coupled Learning in Elastic Networks

Coupled learning is a contrastive scheme for tuning the properties of individual elements within a network in order to achieve desired functionality of the system. It takes advantage of physics both to learn using local rules and to "compute" the output response to input data, thus enabling the system to perform decentralized computation without the need for a processor or external memory. We demonstrate a proof-of-concept mechanical network that can learn simple tasks such as self-symmetrizing via iterative tuning of individual spring rest lengths. These mechanical networks could feasibly be scaled and automated to solve increasingly complex tasks, hinting at a new class of smart metamaterials.

cond-mat.soft↗

arXiv2024

Cornerstones are the Key Stones: Using Interpretable Machine Learning to Probe the Clogging Process in 2D Granular Hoppers

The sudden arrest of flow by formation of a stable arch over an outlet is a unique and characteristic feature of granular materials. Previous work suggests that grains near the outlet randomly sample configurational flow microstates until a clog-causing flow microstate is reached. However, factors that lead to clogging remain elusive. Here we experimentally observe over 50,000 clogging events for a tridisperse mixture of quasi-2D circular grains, and utilize a variety of machine learning (ML) methods to search for predictive signatures of clogging microstates. This approach fares just modestly better than chance. Nevertheless, our analysis using linear Support Vector Machines (SVMs) highlights the position of potential arch cornerstones as a key factor in clogging likelihood. We verify this experimentally by varying the position of a fixed (cornerstone) grain, and show that such a grain dictates the size of feasible flow-ending arches, and thus the time and mass of each flow. Positioning this grain correctly can even increase the ejected mass by over 50%. Our findings demonstrate that interpretable ML algorithms like SVMs can uncover meaningful physics even when their predictive power is below the standards of conventional ML practice.

cond-mat.soft↗

arXiv2024

The Equation of Motion for Taut-Line Buzzers

Equations of motion are developed for the oscillatory rotation of a disk suspended between twisted strings kept under tension by a hanging mass, to which additional forces may be applied. In the absence of forcing, damped harmonic oscillations are observed to decay with an exponential time envelope for two different string types. This is consistent with damping caused by string viscosity, rather than air turbulence, and may be quantified in terms of a quality factor. To test the proposed equation of motion and model for viscous damping within the string, we measure both the natural oscillation frequency and the quality factor for widely varied values of string length, string radius, disk moment of inertia, and hanging mass. The data are found to scale in good accord with predictions. A variation where rotational kinetic energy is converted back and forth to spring potential energy is also discussed.

cond-mat.soft↗

arXiv2024

Taut-Line Buzzers with Periodic Forcing

The time-dependent forcing and work per cycle required to drive sinusoidal spinning of a taut-line buzzer is analytically derived, both on and off resonance, from the nonlinear equation of motion. To test predictions, a model experimental system is constructed and characterized in terms of contraction versus twist angle and damped oscillations. The predicted force profile is then approximately implemented by hand. Nearly sinusoidal motion is observed, and the energy injection per cycle needed to maintain steady state oscillations is found to agree with theory. Additional force profiles are implemented, one to maximize non-sinusoidal response and one to maximize the response per operator effort. With the latter, an Aluminum disk of radius 5~cm and height 0.95~cm was spun at a peak speed of over 11,000~RMP for fifteen minutes. The corresponding hand-powered centrifuge system would required $1.5\times$ more force, twice the power, and triple the time in order to run $90\times$ more samples than prior state-of-art.

cond-mat.soft↗

arXiv2023

Physical learning of power-efficient solutions

As the size and ubiquity of artificial intelligence and computational machine learning (ML) models grow, their energy consumption for training and use is rapidly becoming economically and environmentally unsustainable. Neuromorphic computing, or the implementation of ML in hardware, has the potential to reduce this cost. In particular, recent laboratory prototypes of self-learning electronic circuits, examples of ``physical learning machines," open the door to analog hardware that directly employs physics to learn desired functions from examples. In this work, we show that this hardware platform allows for even further reduction of energy consumption by using good initial conditions as well as a new learning algorithm. Using analytical calculations, simulation and experiment, we show that a trade-off emerges when learning dynamics attempt to minimize both the error and the power consumption of the solution--greater power reductions can be achieved at the cost of decreasing solution accuracy. Finally, we demonstrate a practical procedure to weigh the relative importance of error and power minimization, improving power efficiency given a specific tolerance to error.

cond-mat.dis-nn↗

arXiv2023

Bellybutton: Accessible and Customizable Deep-Learning Image Segmentation

The conversion of raw images into quantifiable data can be a major hurdle in experimental research, and typically involves identifying region(s) of interest, a process known as segmentation. Machine learning tools for image segmentation are often specific to a set of tasks, such as tracking cells, or require substantial compute or coding knowledge to train and use. Here we introduce an easy-to-use (no coding required), image segmentation method, using a 15-layer convolutional neural network that can be trained on a laptop: Bellybutton. The algorithm trains on user-provided segmentation of example images, but, as we show, just one or even a portion of one training image can be sufficient in some cases. We detail the machine learning method and give three use cases where Bellybutton correctly segments images despite substantial lighting, shape, size, focus, and/or structure variation across the regions(s) of interest. Instructions for easy download and use, with further details and the datasets used in this paper are available at pypi.org/project/Bellybuttonseg.

cs.CV↗

arXiv2023

Hierarchical bubble size distributions in coarsening wet liquid foams

Coarsening of two-phase systems is crucial for the stability of dense particle packings such as alloys, foams, emulsions or supersaturated solutions. Mean field theories predict an asymptotic scaling state with a broad particle size distribution. Aqueous foams are good model systems for investigations of coarsening-induced structures, because the continuous liquid as well as the dispersed gas phases are uniform and isotropic. We present coarsening experiments on wet foams, with liquid fractions up to their unjamming point and beyond, that are performed under microgravity to avoid gravitational drainage. As time elapses, a self-similar regime is reached where the normalized bubble size distribution is invariant. Unexpectedly, the distribution features an excess of small \textit{roaming} bubbles, mobile within the network of \textit{jammed} larger bubbles. These roaming bubbles are reminiscent of rattlers in granular materials (grains not subjected to contact forces). We identify a critical liquid fraction $ϕ^*$, above which the bubble assembly unjams and the two bubble populations merge into a single narrow distribution of bubbly liquids. Unexpectedly, $ϕ^*$ is larger than the random close packing fraction of the foam $ϕ_{rcp}$. This is because, between $ϕ_{rcp}$ and $ϕ^*$, the large bubbles remain connected due to a weak adhesion between bubbles. We present models that identify the physical mechanisms explaining our observations. We propose a new comprehensive view of the coarsening phenomenon in wet foams. Our results should be applicable to other phase-separating systems and they may also help to control the elaboration of solid foams with hierarchical structures.

cond-mat.soft↗

arXiv2023

A simply solvable model capturing the approach to statistical self-similarity for the diffusive coarsening of bubbles, droplets, and grains

Aqueous foams and a wide range of related systems are believed to coarsen by gas diffusion between neighboring domains into a statistically self-similar scaling state, after the decay of initial transients, such that dimensionless size and shape distributions become time independent and the average grows as a power law. Partial integrodifferential equations for the time evolution of the size distribution for such phase separating systems can be formulated for arbitrary initial conditions, but these are cumbersome for analyzing data on non-scaling state preparations. Here we show that essential features of the approach to the scaling state can be captured by an exactly-solvable ordinary differential equation for the evolution of the average bubble size. The key ingredient is to characterize the the bubble size distribution approximately, using the average size of all bubbles and the average size of the critical bubbles, which instantaneously neither grow nor shrink. The difference between these two averages serves as a proxy for the width of the size distribution. To test our model, we compare with data for quasi-two dimensional dry foams created with three different initial amounts of polydispersity. This allows us to readily identify the critical radius from the average area of six-sided bubbles, whose growth rate is zero by the von~Neumann law. The growth of the average and critical radii agree quite well with exact solution, though the most monodisperse sample crosses over to the scaling state faster than expected. A simpler approximate solution of our model performs equally well. Our approach is applicable to 3d foams, which we demonstrate by re-analyzing prior data, as well as to froths of dilute droplets and to phase separation kinetics for more general systems such as emulsions, binary mixtures, and alloys.

cond-mat.soft↗

arXiv2023

Twist and Measure: Characterizing the Effective Radius of Strings and Bundles under Twisting Contraction

We test the standard model for the length contraction of a bundle of strings under twist, and find deviation that is significantly greater than typically appreciated and that has a different nature at medium and large twist angles. By including volume conservation, we achieve better fits to data for single-, double-, and triple-stranded bundles of Nylon monofilament as an ideal test case. This gives a well-defined procedure for extracting an effective twist radius that characterizes contraction behavior. While our approach accounts for the observed faster-than-expected contraction up to medium twist angles, we also find that the contraction is nevertheless slower than expected at large twist angles for both Nylon monofilament bundles and several other string types. The size of this effect varies with the individual-string braid structure and with the number of strings in the bundle. We speculate that it may be related to elastic deformation within the material. However, our first modeling attempt does not fully capture the observed behavior.

cond-mat.soft↗

arXiv2023

Machine learning-informed structuro-elastoplasticity predicts ductility of disordered solids

All solids yield under sufficiently high mechanical loads. Below yield, the mechanical responses of all disordered solids are nearly alike, but above yield every different disordered solid responds in its own way. Brittle systems can shatter without warning, like ordinary window glass, or exhibit strain localization prior to fracture, like metallic or polymeric glasses. Ductile systems, e.g. foams like shaving cream or emulsions like mayonnaise, can flow indefinitely with no strain localization. While there are empirical strategies for tuning the degree of strain localization, there is no framework that explains their effectiveness or limitations. We show that Structuro-Elastoplastic (StEP) models provide microscopic understanding of how strain localization depends on the interplay of structure, plasticity and elasticity.

cond-mat.soft↗

arXiv2023

Aqueous foams in microgravity, measuring bubble sizes

The paper describes a study of wet foams in microgravity whose bubble size distribution evolves due to diffusive gas exchange. We focus on the comparison between the size of bubbles determined from images of the foam surface and the size of bubbles in the bulk foam, determined from Diffuse Transmission Spectroscopy (DTS). Extracting the bubble size distribution from images of a foam surface is difficult so we have used three different procedures : manual analysis, automatic analysis with a customized Python script and machine learning analysis. Once various pitfalls were identified and taken into account, all the three procedures yield identical results within error bars. DTS only allows the determination of an average bubble radius which is proportional to the photon transport mean free path $\ell^*$. The relation between the measured diffuse transmitted light intensity and {$\ell^*$} previously derived for slab-shaped samples of infinite lateral extent does not apply to the cuboid geometry of the cells used in the microgravity experiment. A new more general expression of the diffuse intensity transmitted with specific optical boundary conditions has been derived and applied to determine the average bubble radius. The temporal evolution of the average bubble radii deduced from DTS and of the same average radii of the bubbles measured at the sample surface are in very good agreement throughout the coarsening. Finally, ground experiments were performed to compare bubble size distributions in a bulk wet foam and at its surface at times so short that diffusive gas exchange is insignificant. They were found to be similar, confirming that bubbles seen at the surface are representative of the bulk foam bubbles.

cond-mat.soft↗

arXiv2023

Average outpouring velocity and flow rate of grains discharged from a tilted quasi-2D silo

The flow of granular materials through constricted openings is important in many natural and industrial processes. These complex flows - featuring dense, dissipative flow in the bulk but low-dissipation, low density outpouring in the vicinity of the orifice - have long been characterized empirically by the Beverloo rule and, recently, modeled successfully using energy balance. The dependence of flow rate on the silo's angle with respect to gravity, however, is not captured by current models. We experimentally investigate the role of tilt angle in this work using a quasi-2D monolayer of grains in a silo. We measure mass flow rate, the average exit velocities of grains, and the packing fraction along the orifice with varying tilt angles. We propose a model that describes our results (and earlier findings with 3D systems [H. G. Sheldon and D. J. Durian, Granul. Matter 12, 579 (2010)]) by considering the dependence of outpouring speed and angle with respect to the orifice angle and, importantly, the angle of stagnant zones adjacent to the orifice. We conclude by posing questions about possible extensions of our model in order to describe spatial variations of exit velocity and density along the orifice cross section.

cond-mat.soft↗