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Found 23 papers, 17 papers with code
Symbolic Regression on FPGAs for Fast Machine Learning Inference
The high-energy physics community is investigating the feasibility of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to improve physics sensitivity while meeting data processing latency limitations.
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
PySR was developed to democratize and popularize symbolic regression for the sciences, and is built on a high-performance distributed back-end, a flexible search algorithm, and interfaces with several deep learning packages.
Learning Integrable Dynamics with Action-Angle Networks
Here, we propose an alternative construction for learned physical simulators that are inspired by the concept of action-angle coordinates from classical mechanics for describing integrable systems.
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study
We propose parameterizing the population distribution of the gravitational wave population modeling framework (Hierarchical Bayesian Analysis) with a normalizing flow.
Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks
For each test set of 1, 000 sightlines, the BGNN infers the individual $\kappa$ posteriors, which we combine in a hierarchical Bayesian model to yield constraints on the hyperparameters governing the population.
A Neural Network Subgrid Model of the Early Stages of Planet Formation
Planet formation is a multi-scale process in which the coagulation of $\mathrm{\mu m}$-sized dust grains in protoplanetary disks is strongly influenced by the hydrodynamic processes on scales of astronomical units ($\approx 1. 5\times 10^8 \,\mathrm{km}$).
$\texttt{Mangrove}$: Learning Galaxy Properties from Merger Trees
Efficiently mapping baryonic properties onto dark matter is a major challenge in astrophysics.
The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
Our results can be useful for using upcoming SZ surveys (e. g., SO, CMB-S4) and galaxy surveys (e. g., DESI and Rubin) to constrain the nature of baryonic feedback.
Automated discovery of interpretable gravitational-wave population models
This demonstrates a strategy to automatically discover interpretable population models in the ever-growing GW catalog, which can potentially be applied to other astrophysical phenomena.
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys.
Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks
We train neural networks on the IllustrisTNG-300 cosmological simulation to predict the continuous electron pressure field in galaxy clusters from gravity-only simulations.
Rediscovering orbital mechanics with machine learning
We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton's law of gravitation.
Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter
Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines $Y_\mathrm{SZ}$ and concentration of ionized gas ($c_\mathrm{gas}$): $M \propto Y_\mathrm{conc}^{3/5} \equiv Y_\mathrm{SZ}^{3/5} (1-A\, c_\mathrm{gas})$.
Learned Coarse Models for Efficient Turbulence Simulation
We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the comparably low resolutions across various scientifically relevant metrics.
Learned Simulators for Turbulence
We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the same low resolutions across various scientifically relevant metrics.
Unsupervised Resource Allocation with Graph Neural Networks
We present an approach for maximizing a global utility function by learning how to allocate resources in an unsupervised way.
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optical surveys such as the upcoming Vera C. Rubin Observatory.
A Bayesian neural network predicts the dissolution of compact planetary systems
Our model, trained directly from short N-body time series of raw orbital elements, is more than two orders of magnitude more accurate at predicting instability times than analytical estimators, while also reducing the bias of existing machine learning algorithms by nearly a factor of three.
Anomaly Detection for Multivariate Time Series of Exotic Supernovae
Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements.
Predicting the long-term stability of compact multiplanet systems
Our Stability of Planetary Orbital Configurations Klassifier (SPOCK) predicts stability using physically motivated summary statistics measured in integrations of the first $10^4$ orbits, thus achieving speed-ups of up to $10^5$ over full simulations.
Earth and Planetary Astrophysics
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network
This paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class, and no supervision is given for the negative example.
Discovering Symbolic Models from Deep Learning with Inductive Biases
The technique works as follows: we first encourage sparse latent representations when we train a GNN in a supervised setting, then we apply symbolic regression to components of the learned model to extract explicit physical relations.
Lagrangian Neural Networks
Accurate models of the world are built upon notions of its underlying symmetries.
