66 citations · 227 across the 12 of their papers we have counts for
14 papers
SNIP++: Fine-Grained Symbolic-Numerical Alignment for Symbolic Regression
Benjamin Léger, Shubham Gupta, Samy Mammeri +3
Mathematical expressions and the numerical behavior they produce are two views of the same underlying function, and connecting them is central to scientific discovery. Symbolic Reg…
Recovering Wasted Compute in Autoresearch Agents
Au Kwok Chun, Abhigyan Acherjee, Amrutha Rao +4
A slew of recent works develop agents for solving research problems end-to-end, a paradigm increasingly referred to as autoresearch. Such agents have inspired large industry invest…
Towards Diverse Scientific Hypothesis Search with Large Language Models
Haorui Wang, Parshin Shojaee, Kazem Meidani +7
Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses. Yet in many disc…
Zero-shot Multivariate Time Series Forecasting Using Tabular Prior Fitted Networks
Mayuka Jayawardhana, Nihal Sharma, Kazem Meidani +3
Tabular foundation models, particularly Prior-data Fitted Networks like TabPFN have emerged as the leading contender in a myriad of tasks ranging from data imputation to label pred…
Multi-Modal Learning meets Genetic Programming: Analyzing Alignment in Latent Space Optimization
Benjamin Léger, Kazem Meidani, Christian Gagné
Symbolic regression (SR) aims to discover mathematical expressions from data, a task traditionally tackled using Genetic Programming (GP) through combinatorial search over symbolic…
LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models
Parshin Shojaee, Ngoc-Hieu Nguyen, Kazem Meidani +3
Scientific equation discovery is a fundamental task in the history of scientific progress, enabling the derivation of laws governing natural phenomena. Recently, Large Language Mod…
LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
Parshin Shojaee, Kazem Meidani, Shashank Gupta +2
Mathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines. However, discovering such insightful equation…
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
Kazem Meidani, Parshin Shojaee, Chandan K. Reddy +1
In an era where symbolic mathematical equations are indispensable for modeling complex natural phenomena, scientific inquiry often involves collecting observations and translating…
Transformer-based Planning for Symbolic Regression
Parshin Shojaee, Kazem Meidani, Amir Barati Farimani +1
Symbolic regression (SR) is a challenging task in machine learning that involves finding a mathematical expression for a function based on its values. Recent advancements in SR hav…
Transformer for Partial Differential Equations' Operator Learning
Zijie Li, Kazem Meidani, Amir Barati Farimani
Data-driven learning of partial differential equations' solution operators has recently emerged as a promising paradigm for approximating the underlying solutions. The solution ope…
MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning
Parand Akbari, Francis Ogoke, Ning-Yu Kao +4
Characterizing meltpool shape and geometry is essential in metal Additive Manufacturing (MAM) to control the printing process and avoid defects. Predicting meltpool flaws based on…
Graph Neural Networks Accelerated Molecular Dynamics
Zijie Li, Kazem Meidani, Prakarsh Yadav +1
Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and structure of matter. Since the resolution of MD is atomic-scale, achieving long time-scale…
Graph Convolutional Neural Networks for Body Force Prediction
Francis Ogoke, Kazem Meidani, Amirreza Hashemi +1
Many scientific and engineering processes produce spatially unstructured data. However, most data-driven models require a feature matrix that enforces both a set number and order o…
Data-driven Identification of 2D Partial Differential Equations using extracted physical features
Kazem Meidani, Amir Barati Farimani
Many scientific phenomena are modeled by Partial Differential Equations (PDEs). The development of data gathering tools along with the advances in machine learning (ML) techniques…