PIAD-SRNN: Physics-Informed Adaptive Decomposition in State-Space RNN

• URL: http://arxiv.org/abs/2412.00994v2
• Date: Thu, 10 Jul 2025 18:37:02 GMT
• Title: PIAD-SRNN: Physics-Informed Adaptive Decomposition in State-Space RNN
• Authors: Ahmad Mohammadshirazi, Pinaki Prasad Guha Neogi, Rajiv Ramnath,
• Abstract summary: Time series forecasting often demands a trade-off between accuracy and efficiency.<n>We propose PIAD-SRNN, a physics-informed adaptive decomposition state-space RNN.<n>We evaluate PIAD-SRNN's performance on indoor air quality datasets.
• Score: 1.3654846342364306
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Time series forecasting often demands a trade-off between accuracy and efficiency. While recent Transformer models have improved forecasting capabilities, they come with high computational costs. Linear-based models have shown better accuracy than Transformers but still fall short of ideal performance. We propose PIAD-SRNN, a physics-informed adaptive decomposition state-space RNN, that separates seasonal and trend components and embeds domain equations in a recurrent framework. We evaluate PIAD-SRNN's performance on indoor air quality datasets, focusing on CO2 concentration prediction across various forecasting horizons, and results demonstrate that it consistently outperforms SoTA models in both long-term and short-term time series forecasting, including transformer-based architectures, in terms of both MSE and MAE. Besides proposing PIAD-SRNN which balances accuracy with efficiency, this paper also provides four curated datasets. Code and data: https://github.com/ahmad-shirazi/DSSRNN

Related papers

• MAWIFlow Benchmark: Realistic Flow-Based Evaluation for Network Intrusion Detection [47.86433139298671]
  This paper introduces MAWIFlow, a flow-based benchmark derived from the MAWILAB v1.1 dataset.<n>The resulting datasets comprise temporally distinct samples from January 2011, 2016, and 2021, drawn from trans-Pacific backbone traffic.<n>Traditional machine learning methods, including Decision Trees, Random Forests, XGBoost, and Logistic Regression, are compared to a deep learning model based on a CNN-BiLSTM architecture.
  arXiv  Detail & Related papers  (2025-06-20T14:51:35Z)
• Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases [0.3495246564946556]
  We build a surrogate model to approximate radiative heat transfer solutions in a 2-D walled domain with participative gases.<n>The originality of this work lays in the adaptation of the inputs of the problem (gas and wall properties) in order to fit with the CNN architecture.<n>The performance of the CNN architecture is compared to a more classical architecture in terms of speed and accuracy.
  arXiv  Detail & Related papers  (2025-06-02T14:06:44Z)
• Comparison of CNN-based deep learning architectures for unsteady CFD acceleration on small datasets [0.0]
  This study compares advanced convolutional neural network (CNN) architectures for accelerating unsteady computational fluid dynamics (CFD) simulations. CNNs were evaluated under identical conditions to determine their predictive accuracy and robustness in autoregressive time-series predictions. ConvLSTM-UNet consistently outperformed other models, particularly in difference value calculation, achieving lower maximum errors and stable residuals.
  arXiv  Detail & Related papers  (2025-02-06T03:30:49Z)
• Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks [3.1484174280822845]
  We present a method to jointly solve the sensing and model identification problems with simple implementation, efficient, and robust performance. SINDy-SHRED uses Gated Recurrent Units to model sparse sensor measurements along with a shallow network decoder to reconstruct the full-temporal field from the latent state space. We conduct systematic experimental studies on PDE data such as turbulent flows, real-world sensor measurements for sea surface temperature, and direct video data.
  arXiv  Detail & Related papers  (2025-01-23T02:18:13Z)
• Jacobian-Enforced Neural Networks (JENN) for Improved Data Assimilation Consistency in Dynamical Models [0.0]
  Machine learning-based weather models have shown great promise in producing accurate forecasts but have struggled when applied to data assimilation tasks.<n>This study introduces the Jacobian-Enforced Neural Network (JENN) framework, designed to enhance DA consistency in neural network (NN)-emulated dynamical systems.
  arXiv  Detail & Related papers  (2024-12-02T00:12:51Z)
• Adaptive Physics-Guided Neural Network [0.0]
  This paper introduces an adaptive physics-guided neural network (APGNN) framework for predicting quality attributes from image data. The APGNN adaptively balances data-driven and physics-informed predictions, enhancing model accuracy and robustness across different environments. In real-world experiments, the APGNN consistently demonstrated superior performance in the diverse thermal image dataset.
  arXiv  Detail & Related papers  (2024-11-15T09:28:55Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data [47.14384085714576]
  We introduce gridded pseudo-tokenPs to handle unstructured observations and a processor containing gridded pseudo-tokens that leverage efficient attention mechanisms. Our method consistently outperforms a range of strong baselines on various synthetic and real-world regression tasks involving large-scale data. The real-life experiments are performed on weather data, demonstrating the potential of our approach to bring performance and computational benefits when applied at scale in a weather modelling pipeline.
  arXiv  Detail & Related papers  (2024-10-09T10:00:56Z)
• Scalable Mechanistic Neural Networks [52.28945097811129]
  We propose an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences. By reformulating the original Mechanistic Neural Network (MNN) we reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear. Extensive experiments demonstrate that S-MNN matches the original MNN in precision while substantially reducing computational resources.
  arXiv  Detail & Related papers  (2024-10-08T14:27:28Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Graph Neural Networks for Pressure Estimation in Water Distribution Systems [44.99833362998488]
  Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. We combine physics-based modeling and Graph Neural Networks (GNN), a data-driven approach, to address the pressure estimation problem. Our GNN-based model estimates the pressure of a large-scale WDN in The Netherlands with a MAE of 1.94mH$$O and a MAPE of 7%.
  arXiv  Detail & Related papers  (2023-11-17T15:30:12Z)
• Towards Long-Term predictions of Turbulence using Neural Operators [68.8204255655161]
  It aims to develop reduced-order/surrogate models for turbulent flow simulations using Machine Learning. Different model structures are analyzed, with U-NET structures performing better than the standard FNO in accuracy and stability.
  arXiv  Detail & Related papers  (2023-07-25T14:09:53Z)
• Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting [72.89994745876086]
  This work presents the Online Neuro-Evolution-based Neural Architecture Search (ONE-NAS) algorithm. ONE-NAS is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods.
  arXiv  Detail & Related papers  (2023-02-20T22:25:47Z)
• Transfer Learning in Deep Learning Models for Building Load Forecasting: Case of Limited Data [0.0]
  This paper proposes a Building-to-Building Transfer Learning framework to overcome the problem and enhance the performance of Deep Learning models. The proposed approach improved the forecasting accuracy by 56.8% compared to the case of conventional deep learning where training from scratch is used.
  arXiv  Detail & Related papers  (2023-01-25T16:05:47Z)
• MAgNet: Mesh Agnostic Neural PDE Solver [68.8204255655161]
  Climate predictions require fine-temporal resolutions to resolve all turbulent scales in the fluid simulations. Current numerical model solveers PDEs on grids that are too coarse (3km to 200km on each side) We design a novel architecture that predicts the spatially continuous solution of a PDE given a spatial position query.
  arXiv  Detail & Related papers  (2022-10-11T14:52:20Z)
• Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting [6.295157260756792]
  We introduce the Probabilistic AutoRegressive Neural Networks (PARNN) PARNN is capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns. We evaluate the performance of PARNN against standard statistical, machine learning, and deep learning models, including Transformers, NBeats, and DeepAR.
  arXiv  Detail & Related papers  (2022-04-01T17:57:36Z)
• Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery [62.997667081978825]
  We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions. The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
  arXiv  Detail & Related papers  (2022-03-03T19:56:24Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction [5.346782918364054]
  We propose a novel CNN-based spatial-temporal graph framework GraphCNT to support more efficient and accurate trajectory predictions. In contrast to conventional models, both the spatial and temporal modeling of our model are computed within each local time window. Our model achieves better performance in terms of both efficiency and accuracy as compared with state-of-the-art models on various trajectory prediction benchmark datasets.
  arXiv  Detail & Related papers  (2020-03-16T12:56:12Z)

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.