Fluids, characterized by broken time-reversal and parity symmetries, exhibit odd transport phenomena where longitudinal drivings can induce transverse fluxes. Recently, a mesoscale model called chiral stochastic rotation dynamics (CSRD) has been developed to simulate odd fluids with high computational efficiency. In this work, we verify the Green-Kubo relations for both normal and odd transport coefficients in this model, confirming that this model correctly captures the underlying statistical relationship between macroscopic transport and microscopic fluctuations in odd fluids. This work solidifies the physical foundation of the CSRD model, paving the way for its application in studying the statistical physics and nonequilibrium behavior of odd fluids.

Stochastic ecoevolutionary dynamics under coupled behavioral and environmental feedback

STWCR: Weak collocation regression for revealing hidden stochastic dynamics from single trajectory data

Stochastic dynamics of a bistable vibration energy harvesting system with bilateral barriers

First-principles quantum-dynamical simulations of photoinduced exciton dynamics are carried out using the variational two-layer Gaussian-based multiconfiguration time-dependent Hartree (2L-GMCTDH) method combined with stochastic Langevin dynamics. Analogously to earlier reference calculations [Binder and Burghardt, Faraday Discuss. 221, 406 (2020)], a generalized Frenkel-Holstein Hamiltonian is constructed for a 20-site oligothiophene chain as a minimal model for intra-chain exciton migration in poly-(3-hexylthiophene) (P3HT). Here, exciton quasi-particles undergo polaronic trapping due to local high-frequency modes, while transport is induced by thermal driving due to ring-torsional modes. It is shown that the 2L-GMCTDH simulations provide a highly flexible and efficient framework where the use of multiple explicit local reservoirs can be replaced with multiple Langevin thermostats. The computation of temperature-dependent exciton diffusion coefficients is illustrated, along with the dependence on static disorder.

We discuss how a finite noise correlation time, which can arise through coupling to engineered nonthermal environments, affects the fluctuation-driven response in a superconductor above its critical temperature. Using the phenomenological time-dependent Ginzburg-Landau model, we formulate the stochastic dynamics within the path-integral framework. Our analysis reveals that the transport response can be enhanced when the noise correlation time becomes comparable to the intrinsic relaxation time of the superconductor. The magnitude and character of this effect depend strongly on the system's dimensionality.

Waddington's epigenetic landscape has become one of biology's cornerstone metaphors, widely used both conceptually and computationally. Cell types are often associated with the qualitatively stable points or valleys of this landscape. In previous work, we showed that the molecular noise dominating sub-cellular dynamics can distort and profoundly reshape this landscape. In non-equilibrium systems, an equally profound question arises: to what extent does noise alter the transition paths between valleys in such dynamic landscapes? We tackle this question using a set of illustrative exemplars and show that noise gives rise to paths that differ substantially from the canonical least-action paths calculated under deterministic dynamics. We dissect the dynamics of these exemplars and determine the reactive density and transition currents, which show us, respectively, where and how transitions occur for different realizations of stochastic dynamics. Our analysis unambiguously demonstrates that reaction paths for stochastic dynamics diverge non-trivially from their deterministic least-action paths or simple barrier crossing models.

Minimal interaction conditions for the emergence of biological-like organization.

Epoxy resins exhibit excellent mechanical and thermal properties but suffer from molecular degradation under hygrothermal environments, compromising long-term reliability. Here, we introduce an experiment-informed stochastic molecular dynamics (MD) framework for directly modeling hygrothermal degradation in cross-linked epoxy networks without reactive force fields. Starting from an atomistically cured epoxy structure, C-O bond cleavage is introduced stochastically, with the local cleavage probability enhanced by nearby water molecules to account for their catalytic effect. This approach naturally reproduces the sequential release of singly reacted epoxy units and bisphenol A from doubly reacted units. The simulated evolution of low-molecular-weight species and the accompanying decrease in glass transition temperature agree well with experimental observations. This framework provides a computationally efficient and physically transparent route for simulating long-time scale degradation in epoxy resins.

Efficient patient management in hospitals requires adaptive decision-making under time-varying demand and dynamic service environments. This study proposes a heterogeneous medical patient queueing model that integrates reinforcement learning with stochastic queue dynamics to minimize overall patient waiting time. The model distinguishes between two categories of service providers (SPs): those attending first-time patients and those serving returning patients. Each category may differ in service rate but not in medical specialty. Patient arrivals follow a non-homogeneous Poisson process (NHPP) to capture realistic time-dependent flow variations. A Q-learning framework with a supervised ε-greedy policy is developed to determine optimal operational actions, such as adding or reallocating service providers, based on system state and event type. Separate Q-tables are maintained for arrival and departure events to account for differing cost and reward dynamics. Simulation results demonstrate that the proposed model significantly reduces total waiting time and system cost compared with conventional homogeneous queue models. This approach provides a data-driven mechanism for dynamic hospital queue management and can be extended to broader healthcare resource optimization scenarios.

Understanding plant adaptation is critical under intensifying global aridification. Succulence, a key drought-resistance innovation, has evolved repeatedly across plant lineages, yet its intrinsic genomic drivers remain underexplored. Integrating comprehensive evidence from genomics, ecology, and morphology, we investigate adaptation to aridity in the tree grape genus, Cyphostemma (Vitaceae), whose species span environmental gradients from rainforests to deserts and exhibit wide genomic and phenotypic variation. Utilising genome assemblies of representative Cyphostemma species, we demonstrate that specific long terminal repeat retrotransposon (LTR-RT) lineages thrived through the radiation of Cyphostemma and led to substantial intron expansion, a phenomenon rarely studied in eudicots. The intronic LTR-RT insertions likely enhanced tolerance of genome structural changes, facilitating succulence evolution. Genomes of succulents were further expanded by intergenic LTR-RTs, which exhibit recurrent evolutionary advantages in arid and seasonal habitats. Our study reveals how genomic landscapes are shaped by both intrinsic LTR-RT dynamics and extrinsic environmental forces. Critically, we suggest that stochastic dynamics of LTR-RT communities enhance genomic evolvability, enabling adaptive evolution in plants.

Periodic (Floquet) driving enables Hamiltonian engineering and nonequilibrium phases, but interacting systems eventually heat by absorbing energy from the drive. Disorder can greatly delay this process, yielding long-lived prethermal plateaus. Here, we show that this protection can fail when pulse-train control introduces a second driving frequency and when the disorder fluctuates. Using a natural-abundance ^{13}C nuclear-spin network in diamond, we observe sharp peaks in the late-time heating rate at the double- and triple-spin-flip resonance conditions predicted by bimodal Floquet interference and track their evolution with drive frequency. A switching-noise model attributes the resonant absorption to stochastic electron-spin dynamics that intermittently tune rare nuclear clusters into multiphoton resonance. Our results reveal a resonance-activated limit for disorder-stabilized Floquet phases and suggest new routes to dc-field quantum sensing based on an abrupt breakdown of prethermalization.

Physiological stress fundamentally alters disease susceptibility in aquatic environments. In this paper, we develop a stress-structured epidemiological model where host vulnerability is dynamically driven by water quality. Analytically, we establish that the system exhibits a classic forward bifurcation at , confirming that the basic reproduction number remains a valid threshold for eradication. However, stochastic analysis reveals a critical asymmetry not captured by deterministic thresholds. We show that while predicts stability, the probability of an outbreak depends on the initial physiological state and, in time-varying environments, on the exact time of pathogen introduction. Introducing infection into a stressed sub-population leads to an immediate rapid growth of the disease, whereas introduction into the normal compartment occurs later, with the delay depending on the level of stress.

Multivalent binding employs multiple simultaneous supramolecular interactions, increasing avidity and selectivity compared with monovalent binding. While equilibrium aspects of multivalency are well characterized, nonequilibrium behavior remains poorly understood. By combining experiments on hyaluronic acid polymers with kinetic modeling based on stochastic chemical kinetics and molecular dynamics simulations, we systematically investigate the kinetics of multivalent binding. Notably, we find that both association and dissociation kinetics can be more selective than equilibrium binding. We explain this behavior using a two-step binding model featuring a combination of fast, weak and slow, strong interactions. These findings demonstrate a new approach: superselective targeting based on the association rate instead of the equilibrium state. The kinetic theory and experiments presented here provide a fundamental understanding of multivalent kinetics and establish design rules for superselective targeting in out-of-equilibrium systems.

Biological regulatory networks rely on feedback control to suppress intrinsic noise while remaining sensitive and responsive to external signals, yet whether these objectives can be achieved simultaneously remains unclear. Here, we show that biological feedback networks face an unavoidable constraint: intrinsic fluctuations cannot be arbitrarily suppressed without sacrificing response sensitivity or slowing response speed via feedback control. Using a general framework for stochastic feedback dynamics, we derive a fundamental trade-off that limits how these three performance objectives can be jointly optimized. Theoretical results and numerical simulations demonstrate that this constraint persists across high-dimensional systems. We further show that nonequilibrium, non-gradient dynamics, which are prevalent in biological regulation, can partially relax but never eliminate this limitation, reducing the minimal cost of noise suppression by at most a factor of two. We validate our theory using a biologically motivated activator-inhibitor feedback motif. Together, our results elucidate a fundamental limitation of feedback control in enhancing the information transmission capacity of biological regulatory networks.

Nonlinear fractional and stochastic dynamics of air pollution and mortality with data-driven forecasting

ABSTRACT In this paper, authors present a physics-informed neural networks (PINN) approach for solving the optimal control problem (OCP) of the stochastic Schrödinger equation (SSE), with an emphasis on applications in quantum optics. The SSE models quantum systems under uncertainty, a scenario often encountered in open quantum systems where environmental interactions introduce randomness into the system dynamics. These stochastic dynamics are particularly relevant in quantum optics, where precise control over quantum states is crucial for applications such as quantum computing, secure communication, and measurement protocols. Traditional numerical methods for solving such problems face limitations due to high computational costs and the complexity associated with stochastic partial differential equations. The proposed approach utilises PINN, a type of deep learning model that integrates physical laws directly into the learning process by incorporating loss functions based on governing equations, initial and boundary conditions. The fidelity-based objective function is constructed to achieve the desired quantum state so as to develop an OCP solving algorithm. The proposed work is distinguished between deterministic drift-based training and post-training stochastic validation. Extensive numerical experiments are demonstrated near-unity deterministic fidelity. Further, a systematic architecture study validated that a moderately deep network yielded optimal performance with reduced computational cost. Fidelity dissipation analysis confirmed that the method maintained accuracy even under strong damping. These findings established the PINN as an efficient and physically consistent framework for quantum optimal control in open systems.

• Particle-based iSRD-CSG model for catalytic open-foam simulations. • Validated via low-temperature water-gas shift reaction data. • Two flow regimes found near the strut-scale Reynolds number ≈ 10. • Catalyst density in the washcoat can be reduced by the factor 100 without loss of efficiency. • Higher porosity enhances performance in mass-transfer regimes. We present a technique for particle-based simulation of heterogeneous catalysis in open-cell foam structures, combining isotropic Stochastic Rotation Dynamics (iSRD) with Constructive Solid Geometry (CSG). The method is validated against experimental data for the low-temperature water-gas shift reaction in an open-cell foam modeled as an inverse sphere packing. Analysis of the relation between the Sherwood and Reynolds numbers reveals two distinct regimes that intersect at a strut-scale Reynolds number of approximately 10. For typical parameters from the literature, we show that the catalyst density within the washcoat can be significantly reduced without notable loss of conversion efficiency. Further reduction, however, shifts the system toward the reaction-rate-limited regime, resulting in a marked decline in conversion. For the low-temperature water-gas shift reaction, we additionally vary the porosity to identify optimal foam structures that balance low flow resistance with high conversion efficiency. Large porosity values are found to be advantageous not only in the mass-transfer-limited regime but also in the intermediate regime.

Exploring the potential energy surface to sample transition-stateregions is essential to understanding the atomic processes governingchemical reactivity. Ideally, the dividing surface between the eductand product states can be sampled without requiring predefined collectivevariables. Here, we adapt the stochastic saddle point dynamics (SSPD)algorithm by constraining the accessible configuration space accordingto the number of negative Hessian eigenvalues and evaluate its performanceacross increasingly complex systems. We motivate the adaptation usinga simple two-dimensional model potential and demonstrate how the algorithmcan efficiently sample the isomerization reaction of a Lennard-Jonescluster and the decomposition reactions of isopropyl alcohol. Combiningthe SSPD with automatically differentiable machine-learned interatomicpotentials, we apply the approach to CO dissociation on a Co(001)surface both with and without explicit water solvation. The resultshighlight the role of SSPD as a framework for sampling transition-stateregions in complex systems at finite temperatures and demonstrateits versatility in situations where it is not known a priori whetherthe reaction is governed by energetic or entropic contributions.

The turnpike control in stochastic multi-agent dynamics: A discrete-time approach with exponential integrators