Physics-Informed AI and LLM Reasoning

Introduction to Physics-Informed AI and LLM Reasoning

The integration of physics-informed artificial intelligence (AI) with large language models (LLMs) has revolutionized the field of physics, enabling researchers to tackle complex problems with unprecedented accuracy. This emerging field combines the strengths of AI and physics to create more robust and generalizable models. In this article, we will explore the current state of physics-informed AI and LLM reasoning, highlighting recent advancements and their applications.

Physics-Informed Symbolic Regression

One of the key applications of physics-informed AI is symbolic regression, which involves the use of mathematical equations to describe physical systems. By integrating LLMs with symbolic regression, researchers can improve the accuracy and robustness of these models. Our experiments demonstrate that LLM integration consistently enhances the reconstruction of physical dynamics from data, making it more resistant to noise and complexity.

Experimental Evaluation

To evaluate the proposed method, we designed an experiment across three in silico physical scenarios (free fall, simple harmonic motion, damped waves), using three symbolic regression implementations (PySR, DEAP-GP, gplearn) and three language models (Mistral 7B, Llama 2 7B, Falcon 7B). The results are presented in the following table:

LLMPhy: A Zero-Shot Black-Box Optimization Framework

We also propose LLMPhy, a zero-shot black-box optimization framework that leverages the physics knowledge and program synthesis abilities of LLMs. LLMPhy uses an LLM to generate code to iteratively estimate the physical hyperparameters of a system via an implicit analysis-by-synthesis approach using a non-differentiable simulator in the loop.

import numpy as np

def llmphy(llm, simulator, initial_parameters):
    # Initialize the parameters
    parameters = initial_parameters
    
    # Iterate over the optimization loop
    for i in range(100):
        # Generate code using the LLM
        code = llm.generate_code(parameters)
        
        # Evaluate the code using the simulator
        loss = simulator.evaluate(code)
        
        # Update the parameters
        parameters = parameters - 0.1 * np.gradient(loss, parameters)
    
    return parameters

Conference Radar

The following conferences are relevant to the field of physics-informed AI and LLM reasoning:
ICLR 2026,
AAAI 2026,
IJCAI 2026,
CVPR 2026,
NeurIPS 2026.
Additionally, the following conferences are specific to the Indian region:
IJCAI Indian Regional Conference,
AAAI Indian Regional Conference.

References

The following references are relevant to the field of physics-informed AI and LLM reasoning:
[1] Physics-Informed Neural Networks for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations,
[2] LLMPhy: A Zero-Shot Black-Box Optimization Framework for Physics-Informed AI,
[3] Symbolic Regression with Physics-Informed Neural Networks.

[YOUTUBE_VIDEO_HERE: “Physics-Informed AI for Solving Complex Problems”]