arXiv Machine Learning
Jun 8
Synthics: Synthetic Physics-like Datasets for Machine Learning
arXiv:2606. 06724v1 Announce Type: new Abstract: Representative data is fundamental in machine learning, as limited data hinders generalisation.
Equation discovery with Bayesian tree-adjoining grammars
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arXiv Machine Learning
Jul 14
LLM-PDESR: Robust PDE Discovery via Subdomain Weighted Residuals and LLM-Guided Symbolic Hypothesis Generation
arXiv:2607. 10546v1 Announce Type: new Abstract: Discovering governing partial differential equations (PDEs) from noisy observational data is a fundamental challenge in scientific machine learning.
arXiv Machine Learning
Jul 28
Verbalized Particle Posterior: Bayesian Inference over Natural Language Hypotheses
arXiv:2607. 22961v1 Announce Type: new Abstract: Verbalized Machine Learning (VML) parameterizes a model as a natural-language prompt that an LLM evaluates as f(x; theta).
arXiv Machine Learning
Jun 25
A Probabilistic Framework for LLM-Based Model Discovery
arXiv:2602. 18266v2 Announce Type: replace Abstract: Automated methods for discovering mechanistic simulator models from observational data offer a promising path toward accelerating scientific progress.
arXiv Machine Learning
Jun 25
LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search
arXiv:2606. 25039v1 Announce Type: new Abstract: Recovering governing Ordinary Differential Equations (ODEs) from data is a central challenge in modeling dynamical systems across scientific domains.
arXiv AI
Jul 15
Neuro-Symbolic ODE Discovery with Latent Grammar Flow
arXiv:2604. 16232v2 Announce Type: replace-cross Abstract: Understanding natural and engineered systems often relies on symbolic formulations, such as differential equations, which provide interpretability and transferability beyond black-box models.
arXiv Machine Learning
Aug 31
Probabilistic Symbolic Regression for Equation Discovery via Operator-induced and Regularized Symbolic Forests
The paper presents a probabilistic symbolic regression framework that models mathematical expressions as ensembles of symbolic trees, using a regularizing prior to control complexity and an Occam’s window-based posterior to capture uncertainty across plausible models. It provides theoretical guarantees on posterior concentration, including near‑parametric rates when an exact finite formula exists and oracle results under misspecification. Empirical results show the method outperforms state‑of‑the‑art competitors in predictive accuracy, symbolic complexity, and structural recovery on benchmark scientific equations and a materials discovery task.
arXiv Machine Learning
Jun 3
APIC: Amortized Physics-Informed Calibration using Neural Processes
arXiv:2606. 03355v1 Announce Type: new Abstract: Physics models are inherently imperfect due to misspecified or missing mechanisms, resulting in systematic discrepancies between model predictions and real-world observations.
arXiv Machine Learning
Jun 5
PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability
arXiv:2606. 05191v1 Announce Type: new Abstract: Data-driven equation discovery is fundamentally an inverse problem that seeks to infer the governing differential equations of a system directly from time-series measurements.
arXiv Machine Learning
Aug 11
Bayesian Symbolic Regression with Entropic Reinforcement Learning
arXiv:2608. 09617v1 Announce Type: new Abstract: Symbolic regression is the problem of finding an algebraic expression describing a stochastic dependence of a target variable on a set of inputs.
arXiv Machine Learning
Aug 4
Neural Surrogate HMC: On Using Neural Likelihoods for Hamiltonian Monte Carlo in Simulation-Based Inference
arXiv:2407. 20432v3 Announce Type: replace Abstract: Bayesian inference methods such as Markov Chain Monte Carlo (MCMC) typically require repeated computations of the likelihood function, but in some scenarios this is infeasible and alternative methods are needed.
arXiv Machine Learning
Jul 21
Program Synthesis for Simulation-Based Inference: Joint Model Selection and Parameter Estimation
arXiv:2607. 17540v1 Announce Type: new Abstract: Neural simulation-based inference enables parameter estimation for complex models, but typically requires the user to specify a simulator encoding a fixed model structure.