61 papers
This paper introduces a density-gradient-informed (DGI) projection method combined with a robust penalization strategy to effectively eliminate low-thickness regions and deblur structural edges in variable thickness topology optimization, achieving sharp solid-void transitions with negligible impact on structural compliance.
This paper proposes a regularized ensemble Kalman filter framework that integrates sensor displacement data into stochastic phase-field models of brittle fracture to infer the evolving displacement and phase-field states, thereby correcting model predictions while ensuring physical consistency through a novel regularization step.
This paper constructs a functor to define dynamics for an algebraic model of interacting components, generalizing the AlChemy artificial life model and exploring how category theory can formally connect algebraic structures with dynamical systems in artificial chemistry.
ToolRosetta is a unified framework that automatically transforms heterogeneous open-source code repositories into standardized, secure, and executable Model Context Protocol (MCP) tools, enabling LLM agents to autonomously plan and invoke specialized software for complex tasks with minimal human intervention.
The paper introduces EPIC, a hardware- and physics-co-guided distributed scientific machine learning framework that significantly reduces communication latency and energy consumption while preserving physical fidelity by performing lightweight local encoding and physics-aware decoding with cross-attention for tasks like full-waveform inversion.
This paper presents a Google Gemini 2.0 Flash-based chatbot that automates the setup, execution, and post-processing of two-dimensional finite element eddy current simulations using Gmsh and GetDP, thereby significantly reducing the time required to generate electromagnetic models with customizable geometries and analysis routines.
This paper evaluates the impact of LLM-based news sentiment analysis on stock price prediction, demonstrating that DeBERTa outperforms other models and that an ensemble approach achieves 80% accuracy, while sentiment features provide modest improvements to various time-series forecasting architectures.
HarmonyCell is an end-to-end agent framework that automates single-cell perturbation modeling by combining an LLM-driven semantic unifier to resolve metadata incompatibilities and an adaptive Monte Carlo Tree Search engine to synthesize architectures that handle distribution shifts, thereby achieving high execution success and outperforming expert baselines without manual engineering.
This paper proposes and validates a loop-based algorithm for the online, conflict-free scheduling and routing of automated guided vehicles in pickup and delivery systems, demonstrating its superior or comparable performance to exact, greedy, and metaheuristic methods in terms of solution quality and computational efficiency.
The paper introduces Agora, an AI-powered platform that leverages LLMs to simulate diverse human perspectives on policy issues, enabling users to practice consensus-building and demonstrating through a preliminary study that access to authentic voice explanations significantly enhances problem-solving skills and the quality of collective decisions compared to viewing aggregate data alone.
This paper presents a comprehensive taxonomy and practical guide for selecting numerical differentiation methods suited to various scientific applications, accompanied by the open-source Python package PyNumDiff for differentiating noisy data.
This paper proposes a Bitcoin price prediction model using Combinatorial Fusion Analysis (CFA) to integrate diverse machine learning models via rank-score characteristics and weighted combinations, achieving a superior Mean Absolute Percentage Error (MAPE) of 0.19% that outperforms individual models and existing prediction methods.
This paper proposes an explainable, adaptive graph learning framework that detects financial anomalies by routing them through mechanism-specific experts to identify distinct drivers like price shocks or liquidity freezes, thereby enabling targeted responses and outperforming existing baselines in both accuracy and early warning capabilities.
This paper proposes a cost-effective approach to detecting bias in financial language models by leveraging cross-model patterns to identify bias-revealing inputs early, demonstrating that up to 73% of a model's biased behaviors can be uncovered using only 20% of the input pairs when guided by another model's outputs.
The paper introduces Tau-BNO, a deep learning surrogate framework that rapidly and accurately approximates the computationally intensive Network Transport Model of tau propagation in Alzheimer's disease, enabling efficient parameter inference and mechanistic discovery by reducing simulation time from hours to seconds while outperforming existing sequence models.
This study presents a machine learning framework for predicting steady-state flow through porous media, demonstrating that the Fourier Neural Operator (FNO) outperforms convolutional autoencoders and U-Nets by achieving high accuracy, significant computational speedups over traditional CFD, and mesh-invariant properties ideal for topology optimization.
This paper presents a physics-consistent neural network framework for solving Cosserat elasticity problems in microstructured media, which enforces kinematic constraints during training and utilizes derived stability conditions like quasiconvexity and Legendre-Hadamard inequalities to validate the energetic stability of the learned equilibrium solutions.
This paper presents a GPU-accelerated transient Electromagnetic-Thermal-Mechanical co-simulation solver that enables full-scale, non-homogenized early-stage design of advanced packages, overcoming the limitations of conventional steady-state methods by accurately capturing dynamic signal-induced stress and thermal events to prevent costly late-stage failures.
This paper proposes a novel framework that leverages large language models to extract context-aware semantic signals from corporate disclosures, demonstrating that tracking metric shifts via embedding-based similarity significantly outperforms traditional NER-based methods in predicting financial alpha.
This paper demonstrates that applying a conditional diffusion model to asset return prediction reveals a critical bias-variance tradeoff in factor dimensionality, where an intermediate number of factors yields optimal portfolio generalization and outperforms baseline strategies by balancing underfitting and overfitting.