New method trains deep neural networks for non-interacting kinetic-energy functionals in DFT.
problem Lack of exact relationship between electron density and non-interacting kinetic energy.
method Variational principle to regularize machine-learned density functionals.
result Excellent results on kinetic-energy functionals for various systems.
The paper extends Vlasov kinetic theory to time-dependent dynamics using cosymplectic and cocontact manifolds.
problem Extending Vlasov kinetic theory to time-dependent dynamics.
method Introducing geometric kinetic theories within cosymplectic and cocontact manifolds.
result Alternative realizations of cosymplectic and cocontact kinetic theories linked via Poisson/momentum maps.
Study approximates kinetic VFP in bounded domains using weak compactness.
problem Approximating Vlasov-Fokker-Planck in bounded domains with boundary conditions.
method Weak compactness in weighted Hilbert space, construction of test functions.
result Derives diffusion approximation for Vlasov-Fokker-Planck in bounded domains.
In this paper we propose and study a family of sparsity-inducing penalty functions. Since the penalty functions are related to the kinetic energy in special relativity, we call them \emph{kinetic energy plus} (KEP) functions. We construct the KEP function by using the concave conjugate of a χ2-distance function and …
This paper optimizes paths for generative models using kinetic energy.
problem Improving generative model performance and sample quality.
method Investigating and optimizing Gaussian probability paths with kinetic energy.
result Kinetic optimal Gaussian paths simplify particle trajectories and improve model performance.
Researchers created an accurate kinetic energy functional for materials modeling.
problem Lack of accurate analytic kinetic energy functionals for large-scale ab initio materials modeling.
method Interpretative machine learning of crystal cell-averaged kinetic energy densities guided by a hybrid Gaussian process regression - neural network (GPR-NN) method.
result Constructed an analytic kinetic energy functional that reproduces Kohn-Sham DFT energy-volume curves with sufficient accuracy.
New sampling method on Lie groups converges quickly.
problem Sampling on non-Euclidean Lie groups.
method Kinetic Langevin dynamics with noise added.
result Exponential convergence rate proved under W2 distance. RC flow learns molecular kinetics in low dimensions.
problem Discovering interpretable low-dimensional models of molecular kinetics.
method Normalizing flow for coordinate transformation and Brownian dynamics for kinetics approximation.
result Tractable and trainable model of reduced kinetics in continuous time and space.
Automated method creates compact chemical models from detailed ones, reducing complexity and improving accuracy.
problem Creating accurate low-dimensional chemical kinetic models from detailed ones is time-consuming and requires expert knowledge.
method Machine Learned Optimisation of Chemical Kinetics (MLOCK) algorithm systematically perturbs sub-models to find optimal compact models.
result Compact models (15 species) retain ~87% fidelity to detailed models, outperforming previous methods.
Proposes new genetic algorithm rule for market competition.
problem Market competition genetic algorithm rules.
method Econophysics kinetic market model as an evolutionary algorithm.
result New replacement rule for genetic algorithms.
A fast method learns plasma collision kernels from simulations, improving kinetic models.
problem Improving kinetic models for plasma dynamics beyond the weakly coupled regime.
method Data-driven collisional operator, fast spectral separation method.
result Accurately captures plasma dynamics in moderately coupled regime.
Koopman models improve molecular kinetics analysis from short off-equilibrium simulations.
problem Estimating molecular kinetics and collective variables from short, non-equilibrium trajectories.
method Koopman operator theory and dynamic mode decomposition (DMD) to extend TICA and VA to non-equilibrium data.
result Variationally optimal equilibrium expectation values and slow collective variables can be computed from short simulations.
Compact models for methane/air combustion reduce complexity without sacrificing accuracy.
problem Creating accurate, computationally efficient models for methane combustion.
method Data-oriented three-step methodology: 1) Remove non-essential species, 2) Numerically optimize to key species profiles, 3) Machine learning to refine parameters.
result Produced 19 and 15 species compact models that outperform current state-of-the-art models in accuracy and range of conditions.
Generalizes Szebehely's inverse problem to three dimensions.
problem Finding a potential V for given curves in 3D space.
method Extends traditional Szebehely's problem by allowing more general kinetic energy functions.
result New potential functions can generate integral curves including given curves.
New methods achieve linear convergence on broader convex functions.
problem Optimization on broader convex functions with singular or unbounded second derivatives.
method Discretizations of conformal Hamiltonian dynamics.
result Linear convergence on convex functions with singular or unbounded second derivatives.
Kinetic models predict speculators' strategy can affect market prices.
problem Understanding how speculators' behavior affects market prices in a multi-agent exchange system.
method Developed kinetic equations to model interactions between dealers and speculators, using utility functions and mean quantities.
result Speculators' strategy can drive the price of goods towards a zone with marked utility for their group.
CausalKinetiX identifies stable kinetic models from noisy data.
problem Learning stable and predictive kinetic models from noisy data.
method CausalKinetiX framework for identifying structure from discrete time, noisy observations.
result Causal approach improves generalization and prediction in kinetic systems.
AMORE uses neural operators to efficiently predict multiple thermochemical states in stiff chemical kinetics.
problem Efficiently integrating stiff chemical kinetics systems to reduce computational cost.
method Developed AMORE, a framework of adaptive multi-output operator network with two adaptive loss functions.
result Demonstrated improved accuracy and efficiency in predicting thermochemical states from initial conditions.
This paper uses a kinetic approach to model financial agents and their impact on stock prices and wealth distribution.
problem Understanding power-laws in financial data and the causes behind them.
method A kinetic approach inspired by the Levy-Levy-Solomon model, incorporating model predictive control (MPC) for optimization.
result The stock price distribution exhibits power-law behavior for high-frequency traders and lognormal for long-term investors.
In this article, we briefly review the different aspects and applications of kinetic exchange models in economics and sociology. Our main aim is to show in what manner the kinetic exchange models for closed economic systems were inspired by the kinetic theory of gas molecules. The simple yet powerful framework of kinet…
Novel approaches for studying dynamics of Ising models, focusing on kinetic Ising model.
problem Analyzing dynamics of Ising spin glass models on random networks.
method Variational perturbation and extended Plefka approaches to generating functional.
result Extended Plefka expansion outperforms other methods in all regimes.
VAMPnets uses deep learning to model molecular kinetics from simulations.
problem Computing relevant molecular kinetics from simulations requires expert modeling.
method VAMPnets employs variational approach for Markov processes within neural networks.
result VAMPnets produces accurate kinetic models without requiring manual steps.
Paper proposes a smart neck-band for detecting neck postures using integrated kinematic and kinetic data.
problem Improper neck postures lead to musculoskeletal disorders requiring therapy and rehabilitation.
method Integrated use of kinematic and kinetic data with machine learning algorithms.
result 100% accuracy in predicting neck postures using the proposed platform.
New wealth distribution model based on κ-deformation of Gamma distribution.
problem Modeling wealth distribution in heterogeneous kinetic exchange models.
method Proposed a new four-parameter statistical distribution based on κ-deformation of the Generalized Gamma distribution. result The new distribution accurately represents wealth distribution in heterogeneous kinetic exchange models.
A kinetic model explains firm number distribution following Gibrat's law.
problem Understanding firm number distribution in economies.
method Linear kinetic model and mean field analysis.
result Lognormal distribution emerges as large-time behavior.
Optimizes feature selection for molecular kinetics models.
problem Selecting interpretable features for kinetic models from molecular dynamics simulations.
method Direct optimization of features without constructing a full kinetic model.
result Direct feature optimization leads to more efficient model selection.
A step by step procedure to derive analytically the exact dynamical evolution equations of the probability density functions (PDF) of well known kinetic wealth exchange economic models is shown. This technique gives a dynamical insight into the evolution of the PDF, e.g., allowing the calculation of its relaxation time…
New method uses machine learning to analyze catalyst reactions.
problem Understanding complex reaction mechanisms in catalytic materials.
method Combining transient kinetics and machine learning.
result Correct estimates of micro-kinetic coefficients and mechanism.
We briefly review results on nonlinear kinetic equation of Boltzmann type which describe the evolution of wealth in a simple agents market. The mathematical structure of the underlying kinetic equations allows to use well-known techniques of wide use in kinetic theory of rarefied gases to obtain information on the proc…
Study explores how wealth dynamics change with preferential interactions in kinetic exchange models.
problem Investigate how preferential interactions affect wealth dynamics and distributions in kinetic exchange models.
method Conducted Monte Carlo simulations to explore two types of preferential interactions: one with random selection and another with wealth difference constraint.
result Emergence of quasi-oligarchic societies and segregation into economic classes observed in preferential interactions.
A new probabilistic model for PET image reconstruction.
problem Conventional PET image reconstruction ignores uncertainty in TACs.
method Probabilistic Graphical Modeling (PGM) with gradient-based iterative algorithm.
result Incorporates uncertainty in TACs, improving image reconstruction.
LSS learns molecular trajectories from MD data.
problem Limited integration time steps in MD simulations.
method Three deep learning networks for slow collective variables, dynamics, and configuration reconstruction.
result Generates ultra-long synthetic folding trajectories.
Exact solution found for two-body financial dealer model using kinetic theory.
problem Finding an exact solution for the two-body financial dealer model.
method Exact master-Liouville equations derived using kinetic theory and probability currents.
result Exact order-book profile and average transaction interval derived.
Improved KLMC for sampling under various conditions.
problem Stable simulation of kinetic Langevin dynamics under different parameters.
method Revisited synchronous Wasserstein coupling analysis with stochastic exponential Euler discretization.
result Exponential integrator can simulate kinetic Langevin dynamics in the overdamped regime with proper time acceleration.
Study on kinetic Langevin diffusions and their couplings, showing subtle TV bounds and new non-Markovian couplings.
problem Understanding and quantifying the TV distance between solutions of kinetic Langevin diffusions with different initial values.
method Established new non-Markovian couplings for kinetic Langevin diffusions, derived from optimal coalescence trajectories, and analyzed their TV bounds.
result No Markovian coupling can capture the asymptotic decay rate of the TV distance between solutions of kinetic Langevin diffusions with different initial values.
In this paper we introduce kinetic equations for the evolution of the probability distribution of two goods among a huge population of agents. The leading idea is to describe the trading of these goods by means of some fundamental rules in price theory, in particular by using Cobb-Douglas utility functions for the bina…
Improved sampling for multimodal posterior distributions using stochastic gradient methods.
problem Inadequate exploration of multimodal posterior distributions in stochastic gradient MCMC.
method Proposes a generalized kinetic function for Hamiltonian Monte Carlo to enhance mixing in stochastic gradient MCMC.
result Demonstrates superior exploration of complex multimodal posterior distributions.
We study a minimalist kinetic model for economies. A system of agents with local trading rules display emergent demand behaviour. We examine the resulting wealth distribution to look for non-thermal behaviour. We compare and contrast this model with other similar models.
New methods improve efficiency of sampling algorithms for complex systems.
problem Efficiently sampling from complex, high-dimensional probability distributions.
method Randomized Runge-Kutta-Nyström methods tailored for Hamiltonian flows.
result Quantitative 5/2-order L2-accuracy in approximating Hamiltonian flows. Bayesian inference over admissible histories leads to irreversible kinetics.
problem Modeling irreversible processes in systems with uncertain histories.
method A Gibbs-type measure weighted by energy-dissipation action and observation constraints, interpreted as a Bayesian posterior.
result The measure concentrates on maximum-a-posteriori (MAP) histories, recovering classical deterministic evolution.
In this paper, we consider a simple kinetic model of economy involving both exchanges between agents and speculative trading. We show that the kinetic model admits non trivial quasi-stationary states with power law tails of Pareto type. In order to do this we consider a suitable asymptotic limit of the model yielding a…
Optimal control strategies modify wealth inequality variance.
problem Minimizing wealth inequality variance in a market economy.
method Kinetic models and model predictive control for wealth dynamics.
result Control modifies Pareto index of wealth distribution.
Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density. The functional can accurately dissociate a diatomic, and can be systematically improved with training. Highly accurate self-consistent densities and molecular forces are found, indicating the…
New non-kinetic actions on four-manifolds discovered.
problem Finding non-kinetic smooth homotopy coherent actions on four-manifolds.
method Restricting a nontrivial action on a K3 surface and constructing extensions. result First example of a non-kinetic smooth homotopy coherent action of order two on a four-manifold.
Kinetic exchange models have been successful in explaining the shape of the income/wealth distribution in the economies. However, such models usually make some ad-hoc assumptions when it comes to determining the savings factor. Here, we examine a few models in and out of the domain of standard neo-classical economics t…
Stochastic approach improves neural network training for kinetic simulations.
problem Training neural networks under physical constraints in kinetic fusion simulations.
method Stochastic augmented Lagrangian approach using pyTorch.
result Higher model prediction accuracy achieved compared to fixed penalty method.
A new model connects stochastic effects to economic inequality.
problem Understanding economic inequality through stochastic effects.
method Introducing stochastic effects into a kinetic model based on Langevin and Fokker-Planck formalisms.
result Positive correlations between Gini index and total wealth indicate growing inequality.
KCS improves parametric maps from PET images by reducing noise and variance.
problem Improving the quality of parametric maps from PET images due to noise.
method Kinetic Compressive Sensing (KCS) method based on a hierarchical Bayesian model and novel reconstruction algorithm.
result KCS produces spatially coherent images and parametric maps with lower noise and better contrast.