Study physical work done by isotropic vector forces along isotropic curves.
problem Investigate physical work done by isotropic vector forces.
method Analyze forces represented by isotropic vectors acting along isotropic curves on a manifold with specific metric structures.
result Calculate the work done by isotropic vector forces along isotropic curves.
While physics conveys knowledge of nature built from an interplay between observations and theory, it has been considered less importantly in deep neural networks. Especially, there are few works leveraging physics behaviors when the knowledge is given less explicitly. In this work, we propose a novel architecture call…
ξ-torch simplifies physics-informed learning by providing differentiable functionals.
problem Training physics-informed deep neural networks requires differentiable physical simulations.
method ξ-torch offers a library of differentiable functionals for scientific simulations.
result Improves numerical stability and reduces memory requirements for higher order derivatives.
Discussing AI's difficulty and physics' simplicity, suggesting AI benefits from physics principles.
problem AI difficulty compared to physics simplicity.
method Drawing on physical intuition and theoretical physics to improve AI.
result AI and physics principles are strongly coupled through sparsity.
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.
Survey of integrating physics knowledge into machine learning models.
problem Mitigating data shortage and ensuring physical plausibility.
method Combining physics knowledge with machine learning models.
result Summarizes recent works in physics-informed machine learning.
Recent work has documented the susceptibility of deep learning systems to adversarial examples, but most such attacks directly manipulate the digital input to a classifier. Although a smaller line of work considers physical adversarial attacks, in all cases these involve manipulating the object of interest, e.g., putti…
Sullivan discusses his contributions to math and physics.
problem None explicitly stated in the abstract.
method Personal overview of Dennis Sullivan's work.
result Sullivan's work spans mathematics and physics.
Former physicists share insights on derivatives in interviews.
problem Understanding physics in finance interview questions.
method Interviews with former physicists in finance.
result Compilation of physics-related interview answers.
This work demonstrates a physical attack on a deep learning image classification system using projected light onto a physical scene. Prior work is dominated by techniques for creating adversarial examples which directly manipulate the digital input of the classifier. Such an attack is limited to scenarios where the adv…
This work uses statistical mechanics to explain AI learning.
problem Understanding the statistical principles behind AI learning.
method Starting from sample concentration behaviors, the study applies statistical mechanics principles to AI and machine learning.
result Exponential families and statistical quantities are key in AI and machine learning.
Ray-Singer torsion is a mathematical concept with applications in physics.
problem No specific problem stated in the abstract.
method No specific method stated in the abstract.
result No specific key result stated in the abstract.
Kichenassamy's work spans theoretical physics, from relativity to applications.
problem Clarifying the postulational basis of relativity theories.
method Introducing the C-equivalence principle to replace the strong equivalence principle.
result New insights into measurements in both special and general relativity.
Machine learning knot invariants with physics applications.
problem Understanding relations between knot invariants in physics.
method Machine learning and theoretical physics (Chern-Simons theory, gauge theories).
result New analytic results from Big Data experiments.
Work maximization guides machine learning models in adaptive systems.
problem How machine learning models can be optimized for thermodynamic efficiency.
method Introducing thermodynamic principle to compare with maximum-likelihood principle.
result Maximum-work models are equivalent to maximum-likelihood models in adaptive systems.
This work maps Boltzmann distributions to ARNNs for better physics-based model approximations.
problem Approximating Boltzmann distributions of binary systems.
method Exact mapping of Boltzmann distribution to autoregressive neural network architecture.
result New ARNN architectures derived from physical models show superior performance.
This work discovers governing equations from limited data using physics-informed deep learning.
problem Discovering governing equations from scarce and noisy data for complex systems.
method Physics-informed deep learning framework integrating neural networks, physics embedding, and sparse regression.
result The method effectively identifies governing equations from various spatiotemporal systems with different levels of data scarcity and noise.
This is the introduction I wrote for the multi-authored book "From Riemann to differential geometry and relativity", edited by L. Ji, A. Papadopoulos and S. Yamada (Berlin, Springer verlag, 2017). The book consists of twenty chapters, written by various authors. This introduction, besides giving the information on the …
Physics models integrated into VAEs improve generative performance and extrapolation.
problem Improving generative models with interpretability and robustness.
method Physics-based latent space in VAEs with regularized learning to balance physics and neural network components.
result Generative performance and extrapolation improvements demonstrated on synthetic and real-world datasets.
With the advent of modern data collection and storage technologies, data-driven approaches have been developed for discovering the governing partial differential equations (PDE) of physical problems. However, in the extant works the model parameters in the equations are either assumed to be known or have a linear depen…
CoPhy-PGNN tackles competing PG losses in neural networks for solving eigenvalue problems.
problem Solving eigenvalue problems with competing physics-guided loss functions.
method Learning generalizable solutions using a novel approach to handle competing PG losses.
result Demonstrates the effectiveness of the approach in quantum mechanics and electromagnetic propagation.
Riemann's mathematical papers contain many ideas that arise from physics, and some of them are motivated by problems from physics. In fact, it is not easy to separate Riemann's ideas in mathematics from those in physics. Furthermore, Riemann's philosophical ideas are often in the background of his work on science. The …
Neural model predicts object states and physical parameters from visual observations.
problem Computational models struggle with physical reasoning and adapting to new environments.
method Visual prior predicts particle-based system from visual observations; inference module refines estimates subject to dynamics constraints.
result Model can infer physical properties within a few observations and adapt to unseen scenarios.
Single model learns physics from diverse data.
problem Lack of universal physics models for diverse applications.
method General Physics Transformer (GPhyT) trained on diverse physics data.
result Single model achieves superior performance across multiple physics domains.
Unified access package for fundamental physics datasets simplifies machine learning.
problem Lack of unified access to datasets from multiple fundamental physics disciplines.
method Unified Python package with common interface and reference models.
result Graph-based neural networks perform similarly to dedicated methods on various datasets.
We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -- 2D representations of energy depositions from particles interacting …
Absolute parallelism geometry is frequently used for physical applications. It has two main defects, from the point of view of applications. The first is the identical vanishing of its curvature tensor. The second is that its autoparallel paths do not represent physical trajectories. The present work shows how these de…
This work learns models for population dynamics using variational methods and higher-order quadrature.
problem Modeling population dynamics of physical systems with stochastic and mean-field effects.
method Variational problem to infer gradient fields, combining Monte Carlo sampling with higher-order quadrature rules.
result Accurate prediction of population dynamics over a wide range of parameters.
Reduced order modeling of energetic materials using physics-aware neural networks.
problem Simulating complex spatiotemporal dynamics in energetic materials.
method Physics-aware recurrent convolutions (PARC) combined with latent space projection to accelerate model training and inference.
result Significant decrease in training and inference time with comparable accuracy.
The Brownian bridge serves as a physics-informed prior for solving the Poisson equation.
problem Reconstructing physical fields from limited and noisy data with known governing equations.
method Formalizing inverse problems via Bayesian inference in function spaces using a Brownian bridge Gaussian process.
result The Brownian bridge Gaussian process can be viewed as a physics-constrained prior for the Poisson equation, allowing for a fully Bayesian framework.
Machine learning improves planetary space physics by incorporating physical knowledge.
problem Improving performance and interpretability of machine learning models for planetary space physics.
method Building on a previous semi-supervised physics-based classification, the team used varying data and physical information to improve machine learning performance and interpretability.
result Incorporating physical knowledge improves machine learning performance and interpretability, essential for deriving scientific meaning.
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.
In the present work we demonstrate the application of different physical methods to high-frequency or tick-by-tick financial time series data. In particular, we calculate the Hurst exponent and inverse statistics for the price time series taken from a range of futures indices. Additionally, we show that in a limit orde…
Physics-consistent method improves seismic inversion accuracy.
problem Challenges in seismic full-waveform inversion (FWI) due to ill-posedness and high cost.
method Hybrid approach combining physics-based models with data-driven methodologies, incorporating physics into data augmentation.
result Physics-consistent data-driven inversion yields higher accuracy and better generalization.
Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully resolved. Therefore, reliable and accurate closure models for unresolved physics remains an important requirement for many computational phy…
Anomaly Awareness detects anomalies in particle physics and computer vision.
problem Detect anomalies in complex data sets.
method Modifies cost function to learn normal events and anomalies.
result Effective at identifying new anomalies not previously seen.
Framework augments physical models with deep learning for complex dynamics forecasting.
problem Forecasting complex dynamical phenomena with partial knowledge.
method APHYNITY framework: decomposes dynamics into physical and data-driven components.
result Framework accurately forecasts system evolution and identifies relevant parameters.
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 method learns physical meanings in learned representations for better downstream tasks.
problem Lack of explicit physical meanings in learned representations from self-supervised learning.
method Analysis-by-synthesis approach with coordinated sampling and training.
result Trained models can control articulatory synthesizers to speak like humans and generalize well.
Paper develops error rates for physics-informed learning, comparing it to data-driven methods.
problem Understanding the trade-off between soft penalties and hard constraints in PISL.
method Develops complexity-dependent error rates using the small-ball method.
result Physics-informed estimators have comparable error rates to hard constrained methods, differing only by constants.
Bayesian framework discovers interpretable Lagrangian from data.
problem Discovering physical laws from limited data.
method Sparse Bayesian approach for learning interpretable Lagrangian.
result Automates Hamiltonian discovery from Lagrangian and provides ODE/PDE descriptions.
This work integrates differentiation and integration in Physics-Informed Neural Networks.
problem Solving integro-differential equations and computing integral transforms.
method Augmenting Physics-Informed Neural Networks with automatic integration.
result Solving complex integral transforms and integro-differential equations.
We propose a method to generate audio adversarial examples that can attack a state-of-the-art speech recognition model in the physical world. Previous work assumes that generated adversarial examples are directly fed to the recognition model, and is not able to perform such a physical attack because of reverberation an…
In this work, sequence-to-sequence (seq2seq) models, originally developed for language translation, are used to predict the temporal evolution of complex, multi-physics computer simulations. The predictive performance of seq2seq models is compared to state transition models for datasets generated with multi-physics cod…
Study finds physical priors don't significantly improve ML models for learning latent dynamics.
problem Learning latent dynamics from visual observations without access to the underlying state.
method Benchmarked 17 datasets with visual observations of physical systems using various physically inspired methods alongside baselines.
result Physical priors do not significantly improve standard techniques for learning latent dynamics.
In this paper, we demonstrate a physical adversarial patch attack against object detectors, notably the YOLOv3 detector. Unlike previous work on physical object detection attacks, which required the patch to overlap with the objects being misclassified or avoiding detection, we show that a properly designed patch can s…
Efficient surrogate modeling for complex PDEs with physical laws.
problem High computational cost of repeated PDE simulations.
method LC-prior Gaussian process with POD and RBF-FD.
result Significantly reduced computational cost and improved accuracy.
Proposes ENOs for learning PDE solutions that conserve energy.
problem Learning dynamics that obey physical laws, especially in super-resolution settings.
method Energy-consistent Neural Operators (ENOs) with a novel penalty function inspired by energy-based theory.
result ENOs outperform existing DNN models in predicting solutions from data, especially in super-resolution settings.