Articles

This study presents a model-agnostic framework that pairs deep neural operators and Bayesian experimental design for the accurate prediction of extreme events, such as rogue waves, pandemic spikes and structural ship failures.

Partial differential equations are typically solved on every element of a discretization basis before extracting the desired information, and each input requires one solution. In this study, a strategy is proposed to directly compute the quantities of interest, bypassing full-basis solutions and avoiding repetition over inputs.

Three machine learning methods are developed for discovering physically meaningful dimensionless groups and scaling parameters from data, with the Buckingham Pi theorem as a constraint.

The indeterminacy of edge or surface energy — unknowable for low-symmetry crystals — is avoided by an algebraic system complemented with closure equations, which enables computing algorithms to predict the equilibrium shape of any crystal.

A neural network-based language model of supra-word meaning, that is, the combined meaning of words in a sentence, is proposed. Analysis of functional magnetic resonance imaging and magnetoencephalography data helps identify the regions of the brain responsible for understanding this meaning.

Energy gradients from accurate post-density-functional-theory methods often involve statistical uncertainties. This study proposes an algorithm for structural optimizations when using gradients originating from such methods.

A machine learning interatomic potential model is designed and trained on diverse crystal data comprising 89 elements, enabling materials discovery across a vast chemical space without retraining.

A method is proposed to predict drug-induced single-cell gene-expression profiles with tensor imputation, detect cell type-specific maker genes, and identify the trajectories of regulated pathways considering intercellular heterogeneity.

A family of lattice kinetic schemes is introduced for the simulation of relativistic flows. Taking advantage of GPU acceleration, the scheme allows one to efficiently probe both strongly and weakly interacting regimes, for massive and massless particles.

This study reports a chiral instability topography in highly deformed core–shell spheres. A core–shell model and a scaling law are developed to understand its morphoelastic mechanism, which helps the design of a nature-inspired smart topographic gripper based on chiral localization.

A strategy for cooperation in repeated games, called cumulative reciprocity, is proposed. This strategy is robust with respect to errors, enforces fair outcomes, and evolves in environments that are usually hostile to cooperation.

Conduction of neural impulses along axons in the brain is sped up by a substance called myelin, which changes during development and learning. This study reveals that myelin remodelling coordinates and optimizes neuronal communication.

Designing efficient bike path networks requires balancing multiple constraints. In this study, a demand-driven inverse percolation approach is proposed to generate families of efficient bike path networks taking into account cyclist demand and safety preferences.

A framework for measuring how noise in different outbreak data limits the reliability of estimates of epidemic spread is developed and used to show that death time series are rarely better than case data for inferring COVID-19 transmissibility.

A dynamic network model using data from ten European countries indicates that differences in micro-level social interactions can explain a substantial part of variations in the success of national pandemic policies.

A machine learning approach is presented to identify dominant patterns in disease progression in amyotrophic lateral sclerosis (ALS), Alzheimer’s disease and Parkinson’s disease. The nonlinearity of ALS progression has important clinical implications.

This study suggests that a lack of co-location hinders the formation of ‘weak ties’—which are crucial for information spread—in communication networks on the basis of an analysis of an email network of more than 2,800 university researchers.

A spectrally accurate numerical method for solving partial differential equations (PDEs) on non-uniformly curved surfaces is developed. The method is applied to a PDE model of cell polarization to show that geometric effects allow the existence of unexpected multidomain solutions.

A dimensionality reduction framework, delayed latents across groups (DLAG), is proposed for disentangling the concurrent flow of signals between populations of neurons. DLAG reveals bidirectional communication between visual cortical areas.

The determination of state variables to describe physical systems is a challenging task. A data-driven approach is proposed to automatically identify state variables for unknown systems from high-dimensional observational data.