A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Survey of integrating physics knowledge into machine learning models.
Machine learning improves planetary space physics by incorporating physical knowledge.
New method learns from non-uniform data and partial physical knowledge.
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…
In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems. We will first describe the use of outputs from physics-based models in learning a hybrid-physics-data model. Then, we furt…
AutoKE automates embedding physical knowledge into neural networks for complex engineering problems.
Paper develops error rates for physics-informed learning, comparing it to data-driven methods.
Paper integrates ML with physics models for engineering and environmental challenges.
PID-GAN uses physics knowledge to improve deep learning models' reliability.
Framework augments physical models with deep learning for complex dynamics forecasting.
Enhances neural operators with physics knowledge for more accurate simulations.
Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for data-driven models under all possible training and testing scenarios, and proposes an evaluation implem…
Unified physics-informed learning method improves generalization performance.
Centuries of development in natural sciences and mathematical modeling provide valuable domain expert knowledge that has yet to be explored for the development of machine learning models. When modeling complex physical systems, both domain knowledge and data provide necessary information about the system. In this paper…
This paper introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. This framework, termed physics-guided neural networks (PGNN), leverages the output of physics-based model simulations along with observational features in a hybrid modeling …
Framework uses physics knowledge to improve spatiotemporal prediction with limited data.
Physics-based simulations are often used to model and understand complex physical systems and processes in domains like fluid dynamics. Such simulations, although used frequently, have many limitations which could arise either due to the inability to accurately model a physical process owing to incomplete knowledge abo…
We investigate the dynamics of growth models in terms of dynamical system theory. We analyse some forms of knowledge and its influence on economic growth. We assume that the rate of change of knowledge depends on both the rate of change of physical and human capital. First, we study model with constant savings. The mod…
Improved physics-integrated generative models with noise robustness and fidelity.
We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive paradigm could begin to challenge more traditional approaches elaborated over the years in fields like maths or phy…
Deep learning improves solar energy forecasting using physical and data-driven models.
MUSIC learns coupled systems with sparse data and incomplete physics.
Paper introduces a method to learn physics between digital twins using imperfect models.
We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve supervised learning tasks while ensuring that their predictions do not violate physical laws. This is achieved by introducing an additional lo…
PIML enhances machine learning for subsurface energy systems.
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suit…
Improves machine learning models by incorporating physical laws into feature maps.
Develops experimental design for discovering missing physics in bioreactors.
Understanding and interacting with everyday physical scenes requires rich knowledge about the structure of the world, represented either implicitly in a value or policy function, or explicitly in a transition model. Here we introduce a new class of learnable models--based on graph networks--which implement an inductive…
Deep learning models learn chaotic system dynamics from real and simulated data.
Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically-consistent deep neural network architectures is an open issue. In the spirit of physics-informed NNs, PDE-NetGen package provides new means to automatically translate phys…
pVAE combines physics and machine learning for robust data representations.
PICN learns physical fields from shallow neural networks, improving AI in multi-physical systems.
Paper proposes PI-DAE for missing data imputation in buildings using physics constraints.
These notes form part of a lecture course on gauge theory. The material covered is standard in the physics literature, but perhaps less well-known to mathematicians. The purpose of these notes is to make spontaneous symmetry breaking and the Higgs mechanism of mass generation for elementary particles more easily access…
Gradient estimation techniques applied to programs with randomness in high energy physics.
Recent advances in analysis of subband amplitude envelopes of natural sounds have resulted in convincing synthesis, showing subband amplitudes to be a crucial component of perception. Probabilistic latent variable analysis is particularly revealing, but existing approaches don't incorporate prior knowledge about the ph…
This chapter reviews classic regression methods and their evolution to physics-informed approaches.
This work combines machine learning with physical models to solve inverse problems efficiently.
To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physica…
Novel framework for learning infinitesimal generator of stochastic processes.
This text aims to explain general relativity to geometers who have no knowledge about physics. Using handwritten notes by Michel Vaugon, we construct the bases of the theory.
Active researches are currently being performed to incorporate the wealth of scientific knowledge into data-driven approaches (e.g., neural networks) in order to improve the latter's effectiveness. In this study, the Theory-guided Neural Network (TgNN) is proposed for deep learning of subsurface flow. In the TgNN, as s…
Hybrid model combines physics and data to handle incomplete systems.
We give a survey of our joint ongoing work with Ali Chamseddine, Slava Mukhanov and Walter van Suijlekom. We show how a problem purely motivated by "how geometry emerges from the quantum formalism" gives rise to a slightly noncommutative structure and a spectral model of gravity coupled with matter which fits with expe…
Generative framework learns effective, lower-dimensional models from high-dimensional data.
A new model captures car-following and lane-changing behaviors in traffic.
PINNs can learn trivial solutions; new approach improves performance.