Multiple Physics Pretraining for Physical Surrogate Models

• URL: http://arxiv.org/abs/2310.02994v2
• Date: Tue, 10 Dec 2024 16:25:53 GMT
• Title: Multiple Physics Pretraining for Physical Surrogate Models
• Authors: Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Ruben Ohana, Miles Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Geraud Krawezik, Francois Lanusse, Mariel Pettee, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho,
• Abstract summary: We introduce multiple physics pretraining (MPP), an autoregnostic task-atemporal pretraining approach for physical modeling.<n>In MPP, rather than training one model on a specific physical system, we train a backbone model to predict the dynamics of multiple heterogeneous physical systems.<n>We show that a single MPP-pretrained transformer is able to match or outperform task-specific baselines on both pretraining and downstream tasks.
• Score: 41.26924657687872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling of spatiotemporal systems with transformers. In MPP, rather than training one model on a specific physical system, we train a backbone model to predict the dynamics of multiple heterogeneous physical systems simultaneously in order to learn features that are broadly useful across systems and facilitate transfer. In order to learn effectively in this setting, we introduce a shared embedding and normalization strategy that projects the fields of multiple systems into a shared embedding space. We validate the efficacy of our approach on both pretraining and downstream tasks over a broad fluid mechanics-oriented benchmark. We show that a single MPP-pretrained transformer is able to match or outperform task-specific baselines on all pretraining sub-tasks without the need for finetuning. For downstream tasks, we demonstrate that finetuning MPP-trained models results in more accurate predictions across multiple time-steps on systems with previously unseen physical components or higher dimensional systems compared to training from scratch or finetuning pretrained video foundation models. We open-source our code and model weights trained at multiple scales for reproducibility.

Related papers

• AutoHete: An Automatic and Efficient Heterogeneous Training System for LLMs [68.99086112477565]
  Transformer-based large language models (LLMs) have demonstrated exceptional capabilities in sequence modeling and text generation. Existing heterogeneous training methods significantly expand the scale of trainable models but introduce substantial communication overheads and CPU workloads. We propose AutoHete, an automatic and efficient heterogeneous training system compatible with both single- GPU and multi- GPU environments.
  arXiv  Detail & Related papers  (2025-02-27T14:46:22Z)
• Physics-Guided Foundation Model for Scientific Discovery: An Application to Aquatic Science [13.28811382673697]
  We propose a textittextbfPhysics-textbfGuided textbfFoundation textbfModel (textbfPGFM) that combines pre-trained ML models and physics-based models. We demonstrate the effectiveness of this methodology in modeling water temperature and dissolved oxygen dynamics in real-world lakes.
  arXiv  Detail & Related papers  (2025-02-10T00:48:10Z)
• Test-Time Alignment via Hypothesis Reweighting [56.71167047381817]
  Large pretrained models often struggle with underspecified tasks. We propose a novel framework to address the challenge of aligning models to test-time user intent.
  arXiv  Detail & Related papers  (2024-12-11T23:02:26Z)
• POA: Pre-training Once for Models of All Sizes [33.72644336390202]
  We propose a novel tri-branch self-supervised training framework, termed as POA (Pre-training Once for All) Our approach introduces an innovative elastic student branch into a modern self-distillation paradigm. It achieves state-of-the-art performance using ViT, Swin Transformer and ResNet backbones.
  arXiv  Detail & Related papers  (2024-08-02T06:13:29Z)
• MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining [73.81862342673894]
  Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks. We conduct multi-task supervised pretraining on the SAMRS dataset, encompassing semantic segmentation, instance segmentation, and rotated object detection. Our models are finetuned on various RS downstream tasks, such as scene classification, horizontal and rotated object detection, semantic segmentation, and change detection.
  arXiv  Detail & Related papers  (2024-03-20T09:17:22Z)
• Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models [4.299997052226609]
  Masked particle modeling (MPM) is a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs. We study the efficacy of the method in samples of high energy jets at collider physics experiments.
  arXiv  Detail & Related papers  (2024-01-24T15:46:32Z)
• 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)
• 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)
• eP-ALM: Efficient Perceptual Augmentation of Language Models [70.47962271121389]
  We propose to direct effort to efficient adaptations of existing models, and propose to augment Language Models with perception. Existing approaches for adapting pretrained models for vision-language tasks still rely on several key components that hinder their efficiency. We show that by freezing more than 99% of total parameters, training only one linear projection layer, and prepending only one trainable token, our approach (dubbed eP-ALM) significantly outperforms other baselines on VQA and Captioning.
  arXiv  Detail & Related papers  (2023-03-20T19:20:34Z)
• Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information [77.80071279597665]
  We propose an all-in-one single-stage pre-training approach, named Maximizing Multi-modal Mutual Information Pre-training (M3I Pre-training) Our approach achieves better performance than previous pre-training methods on various vision benchmarks, including ImageNet classification, object detection, LVIS long-tailed object detection, and ADE20k semantic segmentation.
  arXiv  Detail & Related papers  (2022-11-17T18:59:49Z)
• Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving [103.745551954983]
  In this paper, we investigate the transfer performance of various types of self-supervised methods, including MoCo and SimCLR, on three downstream tasks. We find that their performances are sub-optimal or even lag far behind the single-task baseline. We propose a simple yet effective pretrain-adapt-finetune paradigm for general multi-task training.
  arXiv  Detail & Related papers  (2022-09-19T12:15:31Z)
• Multi-Stage Influence Function [97.19210942277354]
  We develop a multi-stage influence function score to track predictions from a finetuned model all the way back to the pretraining data. We study two different scenarios with the pretrained embeddings fixed or updated in the finetuning tasks.
  arXiv  Detail & Related papers  (2020-07-17T16:03:11Z)

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.