A differentiable programming framework for spin models

• URL: http://arxiv.org/abs/2304.01772v2
• Date: Wed, 22 May 2024 10:31:58 GMT
• Title: A differentiable programming framework for spin models
• Authors: Tiago de Souza Farias, Vitor Vaz Schultz, José Carlos Merino Mombach, Jonas Maziero,
• Abstract summary: We introduce a novel framework for simulating spin models using differentiable programming. We focus on three distinct spin systems: the Ising model, the Potts model, and the Cellular Potts model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a novel framework for simulating spin models using differentiable programming, an approach that leverages the advancements in machine learning and computational efficiency. We focus on three distinct spin systems: the Ising model, the Potts model, and the Cellular Potts model, demonstrating the practicality and scalability of our framework in modeling these complex systems. Additionally, this framework allows for the optimization of spin models, which can adjust the parameters of a system by a defined objective function. In order to simulate these models, we adapt the Metropolis-Hastings algorithm to a differentiable programming paradigm, employing batched tensors for simulating spin lattices. This adaptation not only facilitates the integration with existing deep learning tools but also significantly enhances computational speed through parallel processing capabilities, as it can be implemented on different hardware architectures, including GPUs and TPUs.

Related papers

• Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging [75.93960998357812]
  Deep model merging represents an emerging research direction that combines multiple fine-tuned models to harness their capabilities across different tasks and domains. Current model merging techniques focus on merging all available models simultaneously, with weight matrices-based methods being the predominant approaches. We propose a training-free projection-based continual merging method that processes models sequentially.
  arXiv  Detail & Related papers  (2025-01-16T13:17:24Z)
• Transferable Post-training via Inverse Value Learning [83.75002867411263]
  We propose modeling changes at the logits level during post-training using a separate neural network (i.e., the value network) After training this network on a small base model using demonstrations, this network can be seamlessly integrated with other pre-trained models during inference. We demonstrate that the resulting value network has broad transferability across pre-trained models of different parameter sizes.
  arXiv  Detail & Related papers  (2024-10-28T13:48:43Z)
• Generalizable data-driven turbulence closure modeling on unstructured grids with differentiable physics [1.8749305679160366]
  We introduce a framework for embedding deep learning models within a generic finite element solver to solve the Navier-Stokes equations. We validate our method for flow over a backwards-facing step and test its performance on novel geometries. We show that our GNN-based closure model may be learned in a data-limited scenario by interpreting closure modeling as a solver-constrained optimization.
  arXiv  Detail & Related papers  (2023-07-25T14:27:49Z)
• Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture [68.13678918660872]
  We design a more capable parameter-sharing architecture based on matrix product operator (MPO) MPO decomposition can reorganize and factorize the information of a parameter matrix into two parts. Our architecture shares the central tensor across all layers for reducing the model size.
  arXiv  Detail & Related papers  (2023-03-27T02:34:09Z)
• A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models [6.072718806755325]
  We develop an original brain-inspired neural model associating Self-Organizing Maps (SOM) and Hebbian learning in the Reentrant SOM (ReSOM) model. This work also demonstrates the distributed and scalable nature of the model through both simulation results and hardware execution on a dedicated FPGA-based platform.
  arXiv  Detail & Related papers  (2022-01-06T22:02:19Z)
• Differentiable Spline Approximations [48.10988598845873]
  Differentiable programming has significantly enhanced the scope of machine learning. Standard differentiable programming methods (such as autodiff) typically require that the machine learning models be differentiable. We show that leveraging this redesigned Jacobian in the form of a differentiable "layer" in predictive models leads to improved performance in diverse applications.
  arXiv  Detail & Related papers  (2021-10-04T16:04:46Z)
• Using Differentiable Programming for Flexible Statistical Modeling [0.0]
  Differentiable programming has recently received much interest as a paradigm that facilitates taking gradients of computer programs. We show how differentiable programming can enable simple gradient-based optimization of a model by automatic differentiation. This allowed us to quickly prototype a model under time pressure that outperforms simpler benchmark models.
  arXiv  Detail & Related papers  (2020-12-07T12:33:49Z)
• A Differentiable Newton Euler Algorithm for Multi-body Model Learning [34.558299591341]
  We motivate a computation graph architecture that embodies the Newton Euler equations. We describe the used virtual parameters that enable unconstrained physical plausible dynamics. We show that the kinematic parameters, required by previous white-box system identification methods, can be accurately inferred from data.
  arXiv  Detail & Related papers  (2020-10-19T19:30:33Z)
• S2RMs: Spatially Structured Recurrent Modules [105.0377129434636]
  We take a step towards exploiting dynamic structure that are capable of simultaneously exploiting both modular andtemporal structures. We find our models to be robust to the number of available views and better capable of generalization to novel tasks without additional training.
  arXiv  Detail & Related papers  (2020-07-13T17:44:30Z)
• Hybrid modeling: Applications in real-time diagnosis [64.5040763067757]
  We outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models. We are using such models for real-time diagnosis applications.
  arXiv  Detail & Related papers  (2020-03-04T00:44:57Z)

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.