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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, Z. 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.
· 0 citations
SMORE: Stability-Promoting Mesh-Agnostic Model Reduction for Time-Dependent PDEs
This work proposes SMORE, a mesh-agnostic framework for model order reduction of time-dependent PDEs, whose latent dynamics are trained with Lyapunov-guided stability regularization, which promotes stable long-horizon rollouts and theoretical guarantees under the stated structural assumptions.
Yang-Yuan Li, Wei-Chao Li, Shao-Wu Pan
· 0 citations
KATOsuper: Surrogate-accelerated neural topology optimization with sensitivity-consistent Fourier neural operators
KATOsuper, an objective-agnostic framework that couples neural-reparameterized topology optimization with a Sensitivity-Consistent Fourier Neural Operator (SC-FNO), demonstrates 15--110x deployment-time speedup over MATLAB baselines while maintaining competitive optimality, with the clearest gains observed in complex 3...
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.
· 0 citations
Conference
Enhancing Reduced-Order Models in High-Dimensional Engineering Applications through Gradient Boosting Tree
This research explores a novel integration of XGBoost, known for its predictive accuracy and capability to handle complex relationships, with an innovative mapping technique between a multi-dimensional Euclidean space and the Grassmann Manifold, which represents low-dimensional subspaces, making XGBoost suitable with m...
Melika Baghi, Xiao Liu, K. Paynabar
· IISE Annual Conference &... · 0 citations