Explorable INR: An Implicit Neural Representation for Ensemble Simulation Enabling Efficient Spatial and Parameter Exploration

• URL: http://arxiv.org/abs/2504.00904v2
• Date: Mon, 21 Apr 2025 17:27:05 GMT
• Title: Explorable INR: An Implicit Neural Representation for Ensemble Simulation Enabling Efficient Spatial and Parameter Exploration
• Authors: Yi-Tang Chen, Haoyu Li, Neng Shi, Xihaier Luo, Wei Xu, Han-Wei Shen,
• Abstract summary: Current surrogate models fall short in the flexibility of point- or region-based predictions.<n>We propose Explorable INR, a novel implicit neural representation-based surrogate model.<n>We demonstrate that the Explorable INR with the proposed approach for spatial and parameter exploration can significantly reduce computation and memory costs.
• Score: 25.58241100888983
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the growing computational power available for high-resolution ensemble simulations in scientific fields such as cosmology and oceanology, storage and computational demands present significant challenges. Current surrogate models fall short in the flexibility of point- or region-based predictions as the entire field reconstruction is required for each parameter setting, hence hindering the efficiency of parameter space exploration. Limitations exist in capturing physical attribute distributions and pinpointing optimal parameter configurations. In this work, we propose Explorable INR, a novel implicit neural representation-based surrogate model, designed to facilitate exploration and allow point-based spatial queries without computing full-scale field data. In addition, to further address computational bottlenecks of spatial exploration, we utilize probabilistic affine forms (PAFs) for uncertainty propagation through Explorable INR to obtain statistical summaries, facilitating various ensemble analysis and visualization tasks that are expensive with existing models. Furthermore, we reformulate the parameter exploration problem as optimization tasks using gradient descent and KL divergence minimization that ensures scalability. We demonstrate that the Explorable INR with the proposed approach for spatial and parameter exploration can significantly reduce computation and memory costs while providing effective ensemble analysis.

Related papers

• Decentralized Inference for Spatial Data Using Low-Rank Models [4.168323530566095]
  This paper presents a decentralized framework tailored for parameter inference in spatial low-rank models.<n>A key obstacle arises from the spatial dependence among observations, which prevents the log-likelihood from being expressed as a summation.<n>Our approach employs a block descent method integrated with multi-consensus and dynamic consensus averaging for effective parameter optimization.
  arXiv  Detail & Related papers  (2025-02-01T04:17:01Z)
• SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification [17.175947741031674]
  Existing deep learning-based surrogate models facilitate efficient data generation, but fall short in uncertainty quantification, efficient parameter space exploration, and reverse prediction. We introduce SurroFlow, a novel normalizing flow-based surrogate model, to learn the invertible transformation between simulation parameters and simulation outputs. Our framework significantly reduces the computational costs while enhancing the reliability and exploration capabilities of scientific surrogate models.
  arXiv  Detail & Related papers  (2024-07-16T19:08:49Z)
• Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization [3.3998740964877463]
  "Local linear recovery" (LLR) is a weaker form of target function recovery. We prove that functions expressible by narrower DNNs are guaranteed to be recoverable from fewer samples than model parameters.
  arXiv  Detail & Related papers  (2024-06-26T03:08:24Z)
• A Metaheuristic for Amortized Search in High-Dimensional Parameter Spaces [0.0]
  We propose a new metaheuristic that drives dimensionality reductions from feature-informed transformations. DR-FFIT implements an efficient sampling strategy that facilitates a gradient-free parameter search in high-dimensional spaces. Our test data show that DR-FFIT boosts the performances of random-search and simulated-annealing against well-established metaheuristics.
  arXiv  Detail & Related papers  (2023-09-28T14:25:14Z)
• Efficient Model-Free Exploration in Low-Rank MDPs [76.87340323826945]
  Low-Rank Markov Decision Processes offer a simple, yet expressive framework for RL with function approximation. Existing algorithms are either (1) computationally intractable, or (2) reliant upon restrictive statistical assumptions. We propose the first provably sample-efficient algorithm for exploration in Low-Rank MDPs.
  arXiv  Detail & Related papers  (2023-07-08T15:41:48Z)
• Interpretable Neural Networks with Random Constructive Algorithm [3.1200894334384954]
  This paper introduces an Interpretable Neural Network (INN) incorporating spatial information to tackle the opaque parameterization process of random weighted neural networks. It devises a geometric relationship strategy using a pool of candidate nodes and established relationships to select node parameters conducive to network convergence.
  arXiv  Detail & Related papers  (2023-07-01T01:07:20Z)
• Fast Exploration of the Impact of Precision Reduction on Spiking Neural Networks [63.614519238823206]
  Spiking Neural Networks (SNNs) are a practical choice when the target hardware reaches the edge of computing. We employ an Interval Arithmetic (IA) model to develop an exploration methodology that takes advantage of the capability of such a model to propagate the approximation error.
  arXiv  Detail & Related papers  (2022-11-22T15:08:05Z)
• GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations [8.851780016570311]
  We propose a graph neural network-based surrogate model to explore the parameter space of ocean climate simulations. GNN-Surrogate predicts the output field with given simulation parameters. We give evaluations on the MPAS-Ocean simulation to demonstrate the effectiveness and efficiency of GNN-Surrogate.
  arXiv  Detail & Related papers  (2022-02-18T01:51:09Z)
• On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game [140.19656665344917]
  We study the reward-free RL problem, where an agent aims to thoroughly explore the environment without any pre-specified reward function. We tackle this problem under the context of function approximation, leveraging powerful function approximators. We establish the first provably efficient reward-free RL algorithm with kernel and neural function approximators.
  arXiv  Detail & Related papers  (2021-10-19T07:26:33Z)
• Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations [55.50218493466906]
  In this paper, the problem of robust reconfigurable intelligent surface (RIS) system design under changes in data distributions is investigated. Using the notion of invariant risk minimization (IRM), an invariant causal representation across multiple environments is used such that the predictor is simultaneously optimal for each environment. A neural network-based solution is adopted to seek the predictor and its performance is validated via simulations against an empirical risk minimization-based design.
  arXiv  Detail & Related papers  (2021-05-04T21:36:31Z)
• On the Sparsity of Neural Machine Translation Models [65.49762428553345]
  We investigate whether redundant parameters can be reused to achieve better performance. Experiments and analyses are systematically conducted on different datasets and NMT architectures.
  arXiv  Detail & Related papers  (2020-10-06T11:47:20Z)
• An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization [48.5614138038673]
  We propose an efficient and robust bandit-based algorithm called Sub-Sampling (SS) in the scenario of hyper parameter search evaluation. We also develop a novel hyper parameter optimization algorithm called BOSS. Empirical studies validate our theoretical arguments of SS and demonstrate the superior performance of BOSS on a number of applications.
  arXiv  Detail & Related papers  (2020-07-11T03:15:21Z)

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.