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Open access
Sep 2026
Perturbation-Based Implicit Full-Waveform Inversion with Improved Optimization Conditioning
Implicit full-waveform inversion (IFWI) has shown strong potential for seismic velocity reconstruction due to the implicit regularization of neural networks. However, conventional IFWI based on direct modeling represents multi-scale structures within a unified parameter space, where gradients associated with differen...
Yue-Kui Chen, Jian Sun, Zhao-Rui Zhu et al.
· Geophysical Journal Internat... · 0 citations
Hybrid Joint-Selective Optimization: Reduced-Space Levenberg-Marquardt Refinement of Low-Dimensional Parameters of Interest
This paper introduces a hybrid joint-selective optimization (HJSO) framework for large-scale numerical problems in which a small subset of trainable quantities is of primary interest. We partition the full parameter vector into a high-dimensional remaining block and a low-dimensional block of parameters of interest (PO...
M. Shahab, Gabriella Alfa Indahsari, I. Mukhlash et al.
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SMORE: Stability-Promoting Mesh-Agnostic Model Reduction for Time-Dependent PDEs
High-fidelity simulations of time-dependent partial differential equations (PDEs) are computationally expensive, motivating data-driven reduced-order surrogates for many-query tasks such as uncertainty quantification, design optimization, data assimilation, and optimal control. However, existing surrogate models often...
Yang-Yuan Li, Wei-Chao Li, Shao-Wu Pan
· 0 citations
KATOsuper: Surrogate-accelerated neural topology optimization with sensitivity-consistent Fourier neural operators
Topology optimization (TO) remains computationally intensive due to repeated finite element analysis (FEA) evaluations required at each iteration. While neural network-based surrogates offer potential acceleration, existing approaches often suffer from gradient inconsistency between predicted objectives and sensitiviti...
Sheng-Yu Yan, Jasmin Jelovica
· 2 citations
Geometry Meets Physics: Data-Efficient Pre-Training for Unstructured Neural PDE Solvers
Neural surrogate models for Partial Differential Equations (PDEs) on unstructured 3D geometries are often limited by poor generalization and the high cost of generating large-scale training datasets. Consequently, pre-training on massive datasets of related PDE dynamics has emerged as a critical alternative to enhance...
Luis Medrano-Navarro, G. Baldan, Qiang Liu et al.
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Conference
Enhancing Reduced-Order Models in High-Dimensional Engineering Applications through Gradient Boosting Tree
Addressing forward problems with high-fidelity models like Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) presents significant challenges due to their computational demands from high-dimensional spatial discretization. These models are expensive, requiring powerful solvers and extensive computatio...
Melika Baghi, Xiao Liu, K. Paynabar
· IISE Annual Conference &... · 0 citations