arXiv subjects

Ehsan Zeraatkar

Publications and source records attributed to Ehsan Zeraatkar.

5 recordsLinked to original sources

arXiv2026

Physics-Guided Transformer (PGT): Physics-Aware Attention Mechanism for PINNs

Reconstructing continuous physical fields from sparse, irregular observations is a central challenge in scientific machine learning, particularly for systems governed by partial differential equations (PDEs). Existing physics-informed methods typically enforce governing equations as soft penalty terms during optimization, often leading to gradient imbalance, instability, and degraded physical consistency under limited data. We introduce the Physics-Guided Transformer (PGT), a neural architecture that embeds physical structure directly into the self-attention mechanism. Specifically, PGT incorporates a heat-kernel-derived additive bias into attention logits, encoding diffusion dynamics and temporal causality within the representation. Query coordinates attend to these physics-conditioned context tokens, and the resulting features are decoded using a FiLM-modulated sinusoidal implicit network that adaptively controls spectral response. We evaluate PGT on the one-dimensional heat equation and two-dimensional incompressible Navier-Stokes systems. In sparse 1D reconstruction with 100 observations, PGT achieves a relative L2 error of 5.9e-3, significantly outperforming both PINNs and sinusoidal representations. In the 2D cylinder wake problem, PGT uniquely achieves both low PDE residual (8.3e-4) and competitive relative error (0.034), outperforming methods that optimize only one objective. These results demonstrate that embedding physics within attention improves stability, generalization, and physical fidelity under data-scarce conditions.

cs.LG↗

arXiv2026

Frequency-Aware Vision Transformers for High-Fidelity Super-Resolution of Earth System Models

Super-resolution can play an essential role in enhancing the spatial fidelity of Earth System Model outputs, allowing fine-scale structures highly beneficial to climate science to be recovered from coarse simulations. However, traditional deep super-resolution methods, including convolutional and transformer based models, tend to exhibit spectral bias, reconstructing low-frequency content more readily than valuable high-frequency details. In this work, we introduce ViSIR and ViFOR, two frequency-aware frameworks. ViSIR stands for the Vision Transformer-Tuned Sinusoidal Implicit Representation. ViSIR combines vision transformers with sinusoidal activations to mitigate spectral bias. ViFOR stands for the Vision Transformer Fourier Representation Network. ViFOR integrates explicit Fourier based filtering for independent low- and high-frequency learning. Evaluated on the E3SM-HR Earth system dataset across surface temperature, shortwave, and longwave fluxes, these models outperform leading Convolutional NN, Generative Networks, and vanilla transformer baselines, with ViFOR demonstrating up to 2.6~dB improvements in Peak Signal to Noise Ratio and higher Structural Similarity.

cs.CV↗

arXiv2025

ViSIR: Vision Transformer Single Image Reconstruction Method for Earth System Models

Purpose: Earth system models (ESMs) integrate the interactions of the atmosphere, ocean, land, ice, and biosphere to estimate the state of regional and global climate under a wide variety of conditions. The ESMs are highly complex; thus, deep neural network architectures are used to model the complexity and store the down-sampled data. This paper proposes the Vision Transformer Sinusoidal Representation Networks (ViSIR) to improve the ESM data's single image SR (SR) reconstruction task. Methods: ViSIR combines the SR capability of Vision Transformers (ViT) with the high-frequency detail preservation of the Sinusoidal Representation Network (SIREN) to address the spectral bias observed in SR tasks. Results: The ViSIR outperforms SRCNN by 2.16 db, ViT by 6.29 dB, SIREN by 8.34 dB, and SR-Generative Adversarial (SRGANs) by 7.93 dB PSNR on average for three different measurements. Conclusion: The proposed ViSIR is evaluated and compared with state-of-the-art methods. The results show that the proposed algorithm is outperforming other methods in terms of Mean Square Error(MSE), Peak-Signal-to-Noise-Ratio(PSNR), and Structural Similarity Index Measure(SSIM).

cs.CV↗

arXiv2025

Energy-Efficient Transformer Inference: Optimization Strategies for Time Series Classification

The increasing computational demands of transformer models in time series classification necessitate effective optimization strategies for energy-efficient deployment. Our study presents a systematic investigation of optimization techniques, focusing on structured pruning and quantization methods for transformer architectures. Through extensive experimentation on three distinct datasets (RefrigerationDevices, ElectricDevices, and PLAID), we quantitatively evaluate model performance and energy efficiency across different transformer configurations. Our experimental results demonstrate that static quantization reduces energy consumption by 29.14% while maintaining classification performance, and L1 pruning achieves a 63% improvement in inference speed with minimal accuracy degradation. Our findings provide valuable insights into the effectiveness of optimization strategies for transformer-based time series classification, establishing a foundation for efficient model deployment in resource-constrained environments.

cs.LG↗

arXiv2023

Adversarial Attacks on Graph Neural Networks based Spatial Resource Management in P2P Wireless Communications

This paper introduces adversarial attacks targeting a Graph Neural Network (GNN) based radio resource management system in point to point (P2P) communications. Our focus lies on perturbing the trained GNN model during the test phase, specifically targeting its vertices and edges. To achieve this, four distinct adversarial attacks are proposed, each accounting for different constraints, and aiming to manipulate the behavior of the system. The proposed adversarial attacks are formulated as optimization problems, aiming to minimize the system's communication quality. The efficacy of these attacks is investigated against the number of users, signal-to-noise ratio (SNR), and adversary power budget. Furthermore, we address the detection of such attacks from the perspective of the Central Processing Unit (CPU) of the system. To this end, we formulate an optimization problem that involves analyzing the distribution of channel eigenvalues before and after the attacks are applied. This formulation results in a Min-Max optimization problem, allowing us to detect the presence of attacks. Through extensive simulations, we observe that in the absence of adversarial attacks, the eigenvalues conform to Johnson's SU distribution. However, the attacks significantly alter the characteristics of the eigenvalue distribution, and in the most effective attack, they even change the type of the eigenvalue distribution.

eess.SP↗