Economics debated as a natural science.
problem Whether Economics should be considered a natural science.
method Discussion of the debate in a special issue of Euro. Phys. J. Special Topic.
result Economics remains a social science, not a natural science.
Machine learning applied to particle physics, focusing on future research and development.
problem Improving particle physics analysis and event identification.
method Roadmap for machine learning applications, resource requirements, and collaborative initiatives.
result Identification of resource needs and areas for external collaboration.
Deep learning excels in AI but struggles with causal physics.
problem Deep learning struggles with causal relationships in physical sciences.
method Combining Bayesian methods, physical constraints, and causal models.
result Deep learning can mislead in systems with unclear causal relationships.
Explains isometric immersions and their applications.
problem Isometric immersions and their applications in math and physics.
method Historical overview and applications.
result Explains the importance and applications of isometric immersions.
We discuss several multi-agent models that have their origin in the kinetic exchange theory of statistical mechanics and have been recently applied to a variety of problems in the social sciences. This class of models can be easily adapted for simulations in areas other than physics, such as the modeling of income and …
Teaches math, physics, and machine learning using Calabi-Yau spaces.
problem Understanding Calabi-Yau spaces in geometry, physics, and machine learning.
method Lecture series, colloquia, and seminars.
result Pedagogical introduction to computational geometry, physical implications, and data science of Calabi-Yau manifolds.
Interdisciplinary fields like econophysics and sociophysics can enrich their parent disciplines.
problem Whether interdisciplinary fields can contribute to their parent disciplines.
method Literature review on history and philosophy of science.
result Interdisciplinary fields have a long history and are likely to continue contributing to their parent disciplines.
New method calculates discrete curvature using effective resistances.
problem Calculating discrete curvature on graphs.
method Effective resistances to calculate curvature on graph nodes and links.
result Relation to established discrete curvatures and convergence to continuous curvature.
Riemann's math ideas often come from physics, blending philosophy and science.
problem Separating Riemann's mathematical ideas from their physical origins.
method Overview of Riemann's work, emphasizing physical motivation and philosophical context.
result Riemann's mathematical results are deeply intertwined with physical reasoning and philosophical ideas.
AI+MPS workshop aims to strengthen AI's role in science.
problem AI's potential to enhance scientific discovery and education.
method Proposes activities and strategic priorities to strengthen AI-MPS link.
result AI and science are becoming increasingly intertwined.
AI boosts study of rare weather extremes with lower costs.
problem Difficulty in studying rare weather events due to limited data and models.
method Coupling AI forecasts with physics models using rare-event algorithms.
result Efficiently characterizes very rare events like once-per-millennium heatwaves.
Parsimonious neural networks discover interpretable physical laws from data.
problem Discovering interpretable physical laws from data using machine learning.
method Combining neural networks with evolutionary optimization to balance accuracy and parsimony.
result Developed models for classical mechanics and materials melting temperature prediction.
Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparat…
Most of the econometric and econophysics models have been borrowed from the statistical physics, and as a cosequence, a new interdisciplinary science called econophysics has emerged. In this paper we planned to extend the analogy between different economic processes or phenomena and processes and phenomena from differe…
Machine learning's data-centric philosophy conflicts with natural sciences' standards.
problem Conflict between machine learning's ontology and epistemology and natural sciences' practices.
method Identifying and analyzing contexts where ML can be beneficial or harmful in natural sciences.
result ML can enhance trustworthiness in causal inference but introduces biases in emulation and labeling.
Topology connects to black holes and wormholes.
problem Understanding topological changes in 3D space.
method Direct connection via surgery.
result New insights into black hole and wormhole formation.
A new method directly encodes data into latent space using gradient flow.
problem Suboptimal representations in physical sciences due to encoder inversion.
method Decoder-only approach using gradient flow and ODEs, avoiding integrals.
result Superior data efficiency and explicit encoding compared to traditional autoencoders.
Quantum models improve recommendation systems by being more efficient and interpretable.
problem Improving recommendation systems efficiency and interpretability.
method Apply quantum models to data science, focusing on interpretability and efficiency.
result Quantum models can be exponentially more efficient than probabilistic models for certain datasets.
Research connects topological changes to black hole formation and wormholes.
problem Understanding topological changes in mathematical three-space.
method Direct connection via surgery to black hole and wormhole formation.
result New platform for exploring geometrical physics.
TGDS integrates scientific theory into data science for better model interpretation and discovery.
problem Limited applicability of data science models in scientific problems involving complex phenomena.
method Integrating scientific theory into data science models to improve model effectiveness and interpretability.
result TGDS aims to advance scientific understanding by discovering novel insights.
Researchers use physics methods to predict gaps between computational and statistical problems.
problem Understanding gaps between information-theoretically possible but computationally unsolvable problems.
method Heuristics based on statistical physics.
result Predictions of computational-to-statistical gaps in statistical problems.
Optimal transport calibrates machine learning models for particle physics simulations.
problem Discrepancies between simulation and experimental data limit machine learning effectiveness.
method A model calibration approach based on optimal transport applied to high-dimensional simulations.
result Calibrated high-dimensional representations enable proper calibration of various downstream quantities.
TNet combines DL with physics models to solve inverse problems efficiently.
problem Solving inverse problems with limited data and physics constraints.
method Model-constrained deep learning approach using TNet.
result TNet solutions are as accurate as traditional methods but faster.
Proposes LVGP for multi-source data fusion in science and engineering.
problem Differences in quality and comprehensiveness of data sources.
method Latent Variable Gaussian Process (LVGP) framework.
result Improved predictions for sparse-data problems.
Dataset of Bose-Einstein condensates images aids ML in many-body physics.
problem Understanding solitons in Bose-Einstein condensates.
method Machine learning (ML) framework with convolutional neural networks and physics-informed classifiers.
result Automatic labeling of solitonic excitations in experimental images.
The paper defines machine learning safety and discusses its application in various systems.
problem The lack of a formal definition of safety in machine learning.
method Defining safety in terms of risk, epistemic uncertainty, and harm; examining safety in cyber-physical systems, decision sciences, and data products; discussing strategies for achieving safety.
result Empirical risk minimization is not always sufficient for safety in machine learning.
Paper integrates ML with physics models for engineering and environmental challenges.
problem Complex science and engineering problems require new methodologies combining physics-based models and ML.
method Structured overview of integrating physics-based models with ML techniques.
result Taxonomy of existing techniques and potential research gaps identified.
Improved meta-learning for dynamics using additional structured knowledge.
problem Meta-learning for dynamics with limited raw observations.
method Extended Neural ODE Process model to use privileged information.
result Improved accuracy and calibration on simulated dynamics tasks.
Review of automation's role in chemical discoveries.
problem Improving autonomous discovery in chemistry.
method Classification of discovery types, assessment of autonomy, case studies.
result Rapid advancements in automation and machine learning are transforming experimentation and modeling.
Physics-informed model reduces RBC simulation costs.
problem Computational infeasibility of direct numerical simulations for turbulent systems.
method Combines CNN and recurrent architecture, penalized with PDEs, uses conformal prediction.
result Significant reduction in computational cost for long-term simulations.
This chapter reviews classic regression methods and their evolution to physics-informed approaches.
problem Finding relationships between variables using regression.
method Introduces traditional and physics-informed regression methods, linking them to computational science.
result Regression methods have evolved from purely statistical to incorporating physical knowledge.
PDE-NetGen converts physical equations to neural networks for various scientific problems.
problem Bridging physics and deep learning for efficient neural network architectures.
method Combines symbolic calculus and neural network generation to translate PDEs into NN architectures.
result Generates compact, computationally-efficient physics-informed NN architectures.
New method shows data-driven causal studies can be misleading.
problem Misattribution of causality in data-driven earth science studies.
method Subsample-based ensemble approach for robust causality analysis.
result Transfer entropy-based causal graphs can be spurious.
This work combines machine learning with physical models to solve inverse problems efficiently.
problem Solving inverse problems in the presence of missing physics and recovering parameters.
method Variational autoencoding with a physically structured decoder network and stochastic local approximations.
result The method accelerates inference for Bayesian inverse problems and acts as a regularizer encoding prior physical information.
A physics-based method improves data interpolators and regression tasks.
problem Improving accuracy and efficiency in function learning.
method Inspired by statistical mechanics, introduces corrections to minimize energy.
result Improves performance in interpolation and regression tasks, especially in high-dimensional spaces.
PICN learns physical fields from shallow neural networks, improving AI in multi-physical systems.
problem Challenges in modeling and forecasting multi-physical systems due to data scarcity and noise.
method Physics-informed convolutional network (PICN) combining CNN and physical laws, using deconvolution and convolution layers.
result PICN effectively solves and estimates nonlinear physical operator equations and recovers physical information from noisy observations.
Mnay models situated in the current research landscape of modelling and simulating social processes have roots in physics. This is visible in the name of specialties as Econophysics or Sociophysics. This chapter describes the history of knowledge transfer from physics, in particular physics of self-organization and evo…
Maximum likelihood estimation and a test of fit based on the Anderson-Darling statistic is presented for the case of the power law distribution when the parameters are estimated from a left-censored sample. Expressions for the maximum likelihood estimators and tables of asymptotic percentage points for the A^2 statisti…
Complexity science integrates natural and social sciences to understand socio-economic dynamics.
problem Chronic poverty and lack of understanding of societal development.
method Use of tools from natural sciences to analyze socio-economic factors.
result Integration of natural and social sciences reveals synergistic mechanisms driving societal evolution.
New framework learns physics from output measurements only.
problem Learning governing physics from only output measurements.
method Stochastic calculus, sparse learning, Bayesian statistics, Euler Maruyama scheme.
result Potential to identify governing physics from sparse, noisy, incomplete data.
This work explores using deep NNs to learn quantum systems from probability distributions.
problem Learning quantum systems from limited probability distribution data.
method Using deep neural networks to reconstruct quantum Hamiltonian from probability distributions.
result Deep neural networks can learn quantum Hamiltonians from probability distributions.
PINNs can learn trivial solutions; new approach improves performance.
problem PINNs fail to learn non-trivial solutions in data-scarce settings.
method Developed an alternative sampling approach and new penalty term.
result PINNs can be improved to learn non-trivial solutions with up to 80% fewer collocation points.
New framework bridges climate science and ML for easier climate model emulation.
problem High computational costs and mistrust of ML methods in climate models.
method Integrating climate science and machine learning perspectives to design easy-to-adopt emulators.
result Demonstrated reliability of emulators designed to address specific tasks.
The paper develops tensor learning methods exploiting symmetries of tensor functions.
problem Efficiently handling tensors in various scientific contexts.
method Equivariant machine learning architectures exploiting orthogonal, Lorentz, and symplectic symmetries.
result Equivariant models outperform non-equivariant baselines in time series analysis.
Gradient-based training and pruning for radial basis function networks in materials physics.
problem Interpretable and robust machine learning for materials physics problems.
method Gradient-based training and pruning of radial basis function networks with closed-form optimization criteria.
result Pruned models provide compact and interpretable versions of larger models, offering insights into atom-level migration processes.
LDDNN learns physical dynamics from data without exact solutions.
problem Learning physical dynamics from data without exact solutions.
method LDDNN topology that learns Lagrangian density from data.
result LDDNN can learn physical dynamics from data.
ML PCA detects phase transitions in muon spectroscopy data.
problem Detecting phase transitions in materials using muon spectroscopy data.
method Unsupervised Machine Learning (PCA) applied to asymmetry functions.
result PCA method effectively detects phase transitions in muon spectroscopy experiments.
Generative models synthesize microstructures respecting physical invariances.
problem Synthesizing microstructures given limited images and physical constraints.
method Three generative models: WGAN, physics-informed GAN, and hybrid model.
result Synthesized microstructures respect physical invariances and latent variable constraints.