Papers Model Discovery
“Model Discovery” 태그가 달린 논문 87편 · 필터 해제
Exploiting Leaderboards for Large-Scale Distribution of Malicious Models
While poisoning attacks on machine learning models have been extensively studied, the mechanisms by which adversaries can distribute poisoned models at scale remain largely unexplored. In this paper, we shed light on how…
Model DiscoveryText Generationtext-to-speechText to SpeechEnhancing generalizability of model discovery across parameter space with multi-experiment equation learning (ME-EQL)
Agent-based modeling (ABM) is a powerful tool for understanding self-organizing biological systems, but it is computationally intensive and often not analytically tractable. Equation learning (EQL) methods can derive con…
Model DiscoveryBi-Level optimization for parameter estimation of differential equations using interpolation
Inverse problem or parameter estimation of ordinary differential equations is a process of obtaining the best parameters using experimental measurements of the states. Single (Multiple)-shooting is a type of sequential o…
Model Discoveryparameter estimationNeuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems
Complex piezoelectric systems are foundational in industrial applications. Their performance, however, is challenged by the nonlinear voltage-displacement hysteretic relationships. Efficient characterization methods are,…
Model DiscoveryAutomated Modeling Method for Pathloss Model Discovery
Modeling propagation is the cornerstone for designing and optimizing next-generation wireless systems, with a particular emphasis on 5G and beyond era. Traditional modeling methods have long relied on statistic-based tec…
Kolmogorov-Arnold NetworksmodelModel DiscoverySymbolic RegressionModel Discovery with Grammatical Evolution. An Experiment with Prime Numbers
Machine Learning produces efficient decision and prediction models based on input-output data only. Such models have the form of decision trees or neural nets and are far from transparent analytical models, based on math…
Model DiscoveryAutomatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency
We introduce the first Automatically Differentiable Model Updating (ADiMU) framework that finds any history-dependent material model from full-field displacement and global force data (global, indirect discovery) or from…
modelModel DiscoveryOptimizing Hard Thresholding for Sparse Model Discovery
Many model selection algorithms rely on sparse dictionary learning to provide interpretable and physics-based governing equations. The optimization algorithms typically use a hard thresholding process to enforce sparse a…
Dictionary LearningmodelModel DiscoveryModel Selection+1Discovering dynamical laws for speech gestures
A fundamental challenge in the cognitive sciences is discovering the dynamics that govern behaviour. Take the example of spoken language, which is characterised by a highly variable and complex set of physical movements …
Model DiscoverySymbolic RegressionCoordinated Ramp Metering Control based on Scalable Nonlinear Traffic Dynamics Model Discovery in a Large Network
This study proposes a coordinated ramp metering control framework in large networks based on scalable nonlinear traffic dynamics model discovery. Existing coordinated ramp metering control methods often require accurate …
Model DiscoveryModel Predictive ControlSODAs: Sparse Optimization for the Discovery of Differential and Algebraic Equations
Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by timescale…
Model DiscoveryAutoToM: Automated Bayesian Inverse Planning and Model Discovery for Open-ended Theory of Mind
Theory of Mind (ToM), the ability to understand people's mental variables based on their behavior, is key to developing socially intelligent agents. Current approaches to Theory of Mind reasoning either rely on prompting…
Model DiscoverySyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond
Symbolic regression (SR) is an emerging branch of machine learning focused on discovering simple and interpretable mathematical expressions from data. Although a wide-variety of SR methods have been developed, they often…
feature selectionGPUModel DiscoverySymbolic RegressionA Cutting Mechanics-based Machine Learning Modeling Method to Discover Governing Equations of Machining Dynamics
This paper proposes a cutting mechanics-based machine learning (CMML) modeling method to discover governing equations of machining dynamics. The main idea of CMML design is to integrate existing physics in cutting mechan…
Model DiscoveryNOMTO: Neural Operator-based symbolic Model approximaTion and discOvery
While many physical and engineering processes are most effectively described by non-linear symbolic models, existing non-linear symbolic regression (SR) methods are restricted to a limited set of continuous algebraic fun…
Model DiscoverySymbolic RegressionBoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery
Understanding the world and explaining it with scientific theories is a central aspiration of artificial intelligence research. Proposing theories, designing experiments to test them, and then revising them based on data…
BenchmarkingExperimental DesignModel Discoveryscientific discoveryAutomated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling
Soft biological tissues exhibit a tendency to maintain a preferred state of tensile stress, known as tensional homeostasis, which is restored even after external mechanical stimuli. This macroscopic behavior can be descr…
Model DiscoveryAutoTurb: Using Large Language Models for Automatic Algebraic Model Discovery of Turbulence Closure
Symbolic regression (SR) methods have been extensively investigated to explore explicit algebraic Reynolds stress models (EARSM) for turbulence closure of Reynolds-averaged Navier-Stokes (RANS) equations. The deduced EAR…
global-optimizationModel DiscoverySymbolic RegressionTorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery
Symbolic regression (SR) is a powerful machine learning approach that searches for both the structure and parameters of algebraic models, offering interpretable and compact representations of complex data. Unlike traditi…
GPUModel DiscoveryregressionSymbolic RegressionTowards Model Discovery Using Domain Decomposition and PINNs
We enhance machine learning algorithms for learning model parameters in complex systems represented by ordinary differential equations (ODEs) with domain decomposition methods. The study evaluates the performance of two …
modelModel Discovery