Cover Image

Beyond Language: Applying MLX Transformers to Engineering Physics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kassinos, Stavros, Alexiadis, Alessio
Format:	Preprint
Published:	2024
Subjects:	Computational Engineering, Finance, and Science Machine Learning Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Auxiliary Finite-Difference Residual-Gradient Regularization for PINNs
by: Kassinos, Stavros
Published: (2026)

The Power of Small LLMs in Geometry Generation for Physical Simulations
by: Shafiq, Ossama, et al.
Published: (2025)

CHAT: Beyond Contrastive Graph Transformer for Link Prediction in Heterogeneous Networks
by: Zhang, Shengming, et al.
Published: (2025)

Physics-informed Partitioned Coupled Neural Operator for Complex Networks
by: Wu, Weidong, et al.
Published: (2024)

Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves
by: Pasparakis, George D., et al.
Published: (2026)

Transferable Physics-Informed Representations via Closed-Form Head Adaptation
by: Wong, Jian Cheng, et al.
Published: (2026)

Physics-Informed Deep Contrast Source Inversion: A Unified Framework for Inverse Scattering Problems
by: Sun, Haoran, et al.
Published: (2025)

Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems
by: Sharma, Vinay, et al.
Published: (2025)

Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes
by: Herreros-Martínez, A., et al.
Published: (2024)

Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials
by: Wang, Sifan, et al.
Published: (2024)

Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
by: Tan, Jingye, et al.
Published: (2026)

PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks
by: Zhao, Zhiyuan, et al.
Published: (2023)

Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design
by: Khan, Shahroz, et al.
Published: (2024)

Beyond the Black Box: Interpretability of LLMs in Finance
by: Tatsat, Hariom, et al.
Published: (2025)

Application of Tensor Neural Networks to Pricing Bermudan Swaptions
by: Patel, Raj G., et al.
Published: (2023)

Physics-Informed GCN-LSTM Framework for Long-Term Forecasting of 2D and 3D Microstructure Evolution
by: Razavi, Hamidreza, et al.
Published: (2025)

Adaptive Temporal Fusion Transformers for Cryptocurrency Price Prediction
by: Peik, Arash, et al.
Published: (2025)

Constraining Generative Models for Engineering Design with Negative Data
by: Regenwetter, Lyle, et al.
Published: (2023)

Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs
by: Shi, Guangsi, et al.
Published: (2023)

Scalable physical source-to-field inference with hypernetworks
by: James, Berian, et al.
Published: (2024)

Energy-based physics-informed neural network for frictionless contact problems under large deformation
by: Bai, Jinshuai, et al.
Published: (2024)

A Resolution Independent Neural Operator
by: Bahmani, Bahador, et al.
Published: (2024)

PI-JEPA: Label-Free Surrogate Pretraining for Coupled Multiphysics Simulation via Operator-Split Latent Prediction
by: Yee, Brandon, et al.
Published: (2026)

Multi-fidelity Batch Active Learning for Gaussian Process Classifiers
by: Cutforth, Murray, et al.
Published: (2025)

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations
by: Lei, Bo, et al.
Published: (2025)

Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning
by: Keum, Seunghwan, et al.
Published: (2026)

XLB: A differentiable massively parallel lattice Boltzmann library in Python
by: Ataei, Mohammadmehdi, et al.
Published: (2023)

AI-Driven Performance-to-Design Generation and Optimization of Marine Propellers
by: Chen, Leah, et al.
Published: (2026)

Overcoming the Loss Conditioning Bottleneck in Optimization-Based PDE Solvers: A Novel Well-Conditioned Loss Function
by: Cao, Wenbo, et al.
Published: (2025)

Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints
by: Nair, Siddharth, et al.
Published: (2025)

Sparse Autoencoders as a Steering Basis for Phase Synchronization in Graph-Based CFD Surrogates
by: Hu, Yeping, et al.
Published: (2026)

Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems
by: Hernandez, Quercus, et al.
Published: (2025)

Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks
by: Riva, Stefano, et al.
Published: (2025)

RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations
by: Ouyang, Weihang, et al.
Published: (2025)

Neural Chaos: A Spectral Stochastic Neural Operator
by: Bahmani, Bahador, et al.
Published: (2025)

Equi-Euler GraphNet: An Equivariant, Temporal-Dynamics Informed Graph Neural Network for Dual Force and Trajectory Prediction in Multi-Body Systems
by: Sharma, Vinay, et al.
Published: (2025)

Differentiation Strategies for Acoustic Inverse Problems: Admittance Estimation and Shape Optimization
by: Borrel-Jensen, Nikolas, et al.
Published: (2025)

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials
by: Jeyaraj, Dhananjeyan, et al.
Published: (2025)

Large Language Model Scaling Laws for Neural Quantum States in Quantum Chemistry
by: Knitter, Oliver, et al.
Published: (2025)

Beyond development: Challenges in deploying machine learning models for structural engineering applications
by: Esteghamati, Mohsen Zaker, et al.
Published: (2024)