Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties

• URL: http://arxiv.org/abs/2407.02770v1
• Date: Wed, 3 Jul 2024 02:57:40 GMT
• Title: Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties
• Authors: Ning Liu, Siavash Jafarzadeh, Brian Y. Lattimer, Shuna Ni, Jim Lua, Yue Yu,
• Abstract summary: Large language models (LLMs) bear promise as a fast and accurate material modeling paradigm for evaluation, analysis, and design. We present a physics-based training pipeline that tackles the pathology of data scarcity.
• Score: 10.955525128731654
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models (LLMs) bear promise as a fast and accurate material modeling paradigm for evaluation, analysis, and design. Their vast number of trainable parameters necessitates a wealth of data to achieve accuracy and mitigate overfitting. However, experimental measurements are often limited and costly to obtain in sufficient quantities for finetuning. To this end, we present a physics-based training pipeline that tackles the pathology of data scarcity. The core enabler is a physics-based modeling framework that generates a multitude of synthetic data to align the LLM to a physically consistent initial state before finetuning. Our framework features a two-phase training strategy: (1) utilizing the large-in-amount while less accurate synthetic data for supervised pretraining, and (2) finetuning the phase-1 model with limited experimental data. We empirically demonstrate that supervised pretraining is vital to obtaining accurate finetuned LLMs, via the lens of learning polymer flammability metrics where cone calorimeter data is sparse.

Related papers

• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines [0.0]
  This paper is aimed at using the newly developing field of physics informed machine learning (PIML) to develop models for predicting the remaining useful lifetime (RUL) aircraft engines. We consider the well-known benchmark NASA Commercial Modular Aero-Propulsion System Simulation System (C-MAPSS) data as the main data for this paper. C-MAPSS is a well-studied dataset with much existing work in the literature that address RUL prediction with classical and deep learning methods.
  arXiv  Detail & Related papers  (2024-06-21T19:55:34Z)
• Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures [6.483318568088176]
  PiML method integrates scientific principles and physical laws into deep neural networks to model seismic responses of nonlinear structures. Manipulating the equation of motion helps learn system nonlinearities and confines solutions within physically interpretable results. Result handles complex data better than existing physics-guided LSTM models and outperforms other non-physics data-driven networks.
  arXiv  Detail & Related papers  (2024-02-28T02:16:03Z)
• Fine-Tuned Language Models Generate Stable Inorganic Materials as Text [57.01994216693825]
  Fine-tuning large language models on text-encoded atomistic data is simple to implement yet reliable. We show that our strongest model can generate materials predicted to be metastable at about twice the rate of CDVAE. Because of text prompting's inherent flexibility, our models can simultaneously be used for unconditional generation of stable material.
  arXiv  Detail & Related papers  (2024-02-06T20:35:28Z)
• An Emulator for Fine-Tuning Large Language Models using Small Language Models [91.02498576056057]
  We introduce emulated fine-tuning (EFT), a principled and practical method for sampling from a distribution that approximates the result of pre-training and fine-tuning at different scales. We show that EFT enables test-time adjustment of competing behavioral traits like helpfulness and harmlessness without additional training. Finally, a special case of emulated fine-tuning, which we call LM up-scaling, avoids resource-intensive fine-tuning of large pre-trained models by ensembling them with small fine-tuned models.
  arXiv  Detail & Related papers  (2023-10-19T17:57:16Z)
• A Physics Enhanced Residual Learning (PERL) Framework for Vehicle Trajectory Prediction [5.7215490229343535]
  PERL integrates the strengths of physics-based and data-driven methods for traffic state prediction. It preserves the interpretability inherent to physics-based models and has reduced data requirements. PERL achieves better prediction with a small dataset, compared to the physics model, data-driven model, and PINN model.
  arXiv  Detail & Related papers  (2023-09-26T21:41:45Z)
• Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior [32.74388989649232]
  We study how pre-training could be used for scientific machine learning (SciML) applications. We find that fine-tuning these models yields more performance gains as model size increases.
  arXiv  Detail & Related papers  (2023-06-01T00:32:59Z)
• METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals [151.3601429216877]
  We present an efficient method of pretraining large-scale autoencoding language models using training signals generated by an auxiliary model. We propose a recipe, namely "Model generated dEnoising TRaining Objective" (METRO) The resultant models, METRO-LM, consisting of up to 5.4 billion parameters, achieve new state-of-the-art on the GLUE, SuperGLUE, and SQuAD benchmarks.
  arXiv  Detail & Related papers  (2022-04-13T21:39:15Z)
• MoEfication: Conditional Computation of Transformer Models for Efficient Inference [66.56994436947441]
  Transformer-based pre-trained language models can achieve superior performance on most NLP tasks due to large parameter capacity, but also lead to huge computation cost. We explore to accelerate large-model inference by conditional computation based on the sparse activation phenomenon. We propose to transform a large model into its mixture-of-experts (MoE) version with equal model size, namely MoEfication.
  arXiv  Detail & Related papers  (2021-10-05T02:14:38Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• Learning physically consistent mathematical models from data using group sparsity [2.580765958706854]
  In areas like biology, high noise levels, sensor-induced correlations, and strong inter-system variability can render data-driven models nonsensical or physically inconsistent. We show several applications from systems biology that demonstrate the benefits of enforcing $textitpriors$ in data-driven modeling.
  arXiv  Detail & Related papers  (2020-12-11T14:45:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.