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Found 11 papers, 8 papers with code
Identifying Hamiltonian manifold in neural networks
Our results suggest that the meta-trained model can craft the representation of Hamilton's equation in neural networks which is shared across various dynamical systems with each governed by different physical laws.
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
