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Found 15 papers, 9 papers with code
Exploring how deep learning decodes anomalous diffusion via Grad-CAM
While deep learning has been successfully applied to the data-driven classification of anomalous diffusion mechanisms, how the algorithm achieves the feat still remains a mystery.
Stochastic Resetting Mitigates Latent Gradient Bias of SGD from Label Noise
To mitigate this negative effect, we apply the stochastic resetting method to SGD, inspired by recent developments in the field of statistical physics achieving efficient target searches.
Gaussian Universality in Neural Network Dynamics with Generalized Structured Input Distributions
Bridging the gap between the practical performance of deep learning and its theoretical foundations often involves analyzing neural networks through stochastic gradient descent (SGD).
Inferring the Langevin Equation with Uncertainty via Bayesian Neural Networks
Pervasive across diverse domains, stochastic systems exhibit fluctuations in processes ranging from molecular dynamics to climate phenomena.
Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning
We model our system with a graph neural network (GNN) and employ a meta learning algorithm to enable the model to gain experience over a distribution of systems and make it adapt to new physics.
Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network
Our model employs a novel pairwise attention (PA) mechanism to refine the trajectory representations and a graph transformer to extract heterogeneous interaction weights for each pair of agents.
Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning
How have individuals of social animals in nature evolved to learn from each other, and what would be the optimal strategy for such learning in a specific environment?
Attaining entropy production and dissipation maps from Brownian movies via neural networks
We show that our method accurately measures the EP and creates a dissipation map in two nonequilibrium systems, the bead-spring model and a network of elastic filaments.
Discovering conservation laws from trajectories via machine learning
Invariants and conservation laws convey critical information about the underlying dynamics of a system, yet it is generally infeasible to find them from large-scale data without any prior knowledge or human insight.
Inertial effects on the Brownian gyrator
Another unique feature of the Langevin description is that rotation is maximized at a particular anisotropy while the stability of the rotation is minimized at a particular anisotropy or mass.
Statistical Mechanics
Deep reinforcement learning for feedback control in a collective flashing ratchet
A collective flashing ratchet transports Brownian particles using a spatially periodic, asymmetric, and time-dependent on-off switchable potential.
Learning entropy production via neural networks
This Letter presents a neural estimator for entropy production, or NEEP, that estimates entropy production (EP) from trajectories of relevant variables without detailed information on the system dynamics.
Deep learning reveals hidden interactions in complex systems
Rich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by human scientists.
Historic Emergence of Diversity in Painting: Heterogeneity in Chromatic Distance in Images and Characterization of Massive Painting Data Set
Painting is an art form that has long functioned as a major channel for the creative expression and communication of humans, its evolution taking place under an interplay with the science, technology, and social environments of the times.
Early onset of structural inequality in the formation of collaborative knowledge, Wikipedia
We perform an in-depth analysis on the inequality in 863 Wikimedia projects.
Physics and Society Social and Information Networks
