Ioannis G. Kevrekidis
Authored
(393f) Data-Driven Evolution Equation Reconstruction for Parameter-Dependent Nonlinear Dynamical Systems
2018 AIChE Annual Meeting (ISBN: 978-0-8169-1108-0)
(598a) Towards Global Optimization on Low-Dimensional Surrogates Via Manifold Learning
2018 AIChE Annual Meeting (ISBN: 978-0-8169-1108-0)
(257f) Explicit Model Predictive Control Using Nonlinear Intrinsic Variables
2018 AIChE Annual Meeting (ISBN: 978-0-8169-1108-0)
(658b) Manifold Learning for Measurements across Several Sensors: Alternating Diffusion, Data Fusion and Constructing Nonlinear Observers for Complex Chemical Reaction Networks
2018 AIChE Annual Meeting (ISBN: 978-0-8169-1108-0)
(126e) Recurrent Neural Networks, Numerical Integrators and Nonlinear System Identification
2018 AIChE Annual Meeting (ISBN: 978-0-8169-1108-0)
(675b) Modeling, Optimization, and Control of Bioprocesses Using Optogenetics
2018 AIChE Annual Meeting (ISBN: 978-0-8169-1108-0)
(344a) Coarse-Scale PDEs from Microscopic Observations Via Machine Learning
2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)
(324f) Grey-Box Identification for a Class of Nonlinear Dynamical Systems
2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)
(369i) Multiple Experts: Using the Mahalanobis Metric to Fuse Data from Different Partial Observations
2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)
(193c) When Have Two Networks Learned the Same Task? Data-Driven Transformations between System Representations
2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)
(753d) Data Driven Parameter Reduction
2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)
(110i) Learning Partial Differential Equations from Discrete Space Time Data: Convolutional and Recurrent Networks, and Their Relations to Traditional Numerical Methods
2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)
(373t) Exploring Energy Landscapes Using the Directed Graph Laplacian
2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)
(340e) Learning Coarse-Grained Partial Differential Equations from Fine-Scale Data Via Machine Learning
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
(661f) On the Data-Driven Discovery and Calibration of Closures
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
(299h) PDE+Pinn: Neural Identification and Solution of Partial Differential Equations on Partial Data
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
(278g) Scientific Computation in the Latent Space through Manifold Learning
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
(46a) Dynamical-Systems-Guided Learning of PDEs from Data
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
(402g) Connections between Residual Networks and Explicit Numerical Integrators, with Applications to Identification of Noninvertible Dynamical Systems
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
(278h) From Partial Data to out-of-Bounds Parameter and Observation Estimation with Diffusion Maps and Geometric Harmonics
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
(340f) Assessing the Response of Dynamic Catalytic Surfaces
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
(470f) Learning the Dynamics of Coupled Oscillator Systems through the Discovery of Emergent PDEs Via Artificial Neural Networks and Manifold Learning
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
(132c) Learning What to Learn: Some Data-Driven Twists in Linking System Identification, Manifold Learning, and (possibly) Causality Considerations
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(572f) Transport- and Reaction-Modeling of Nanocarriers for Cancer Therapeutics Via Experimental and in-Silico approaches
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(246f) Emergent Data-Driven Model Reductions for Coupled, Heterogeneous Agent-Based Systems
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(549e) Learning Coarse-Scale ODEs/PDEs from Microscopic Data: What and How Can We Learn It from Data?
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(476d) Computations and Optimization for Catalysts Under Dynamic Operation
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(226a) Learning Partial Differential Equations in Emergent Coordinates
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(303f) Initializing the Internal States of Lstm Neural Networks Via Manifold Learning
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(51f) From Brownian Dynamics Simulations and Experimental Observations of Colloidal Suspensions to Data-Driven Observables and Effective Sdes with Manifold Learning
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(203g) A Data-Driven Approach to Disentangled Parametrization of Relations, with an Application to Multisite Phosphorylation
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(346m) Learning Partial Differential Equations from Multiscale or Experimental Data: A Showcase on Bacterial Chemotaxis
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(560e) What Matters and What Does Not Matter: Parametrizing Common and Sensor-Specific Information across Multiple Sensors in Chemically Reacting Systems
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(284h) Emergent Evolution Equations from (multi-)Puzzle Tiles, with a Drosophila Embryonic Development Example
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(186d) Addressing Well-Posedness in a Data-Driven Manner: Model Problems and Physics-Informed Neural Networks
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(364l) Something Old, Something New: Generative Adversarial Approaches to Conditional Sampling in Complex System Simulations
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(303a) Data-Driven Development of Approximate Inertial Forms and Closures for Coarse-Scale Modeling of Multiphase Flows
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(246c) Pathologies of Neural Networks As Models of Discrete-Time Dynamical Systems
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(287d) Coarse-Grained Dynamics for Epidemics on Adaptive Networks
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(212b) Manifold Learning for Accelerating Coarse-Grained Optimization
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
(673f) Reaction-Diffusion Modeling of Nanocarrier Cocktails for Cancer Therapeutics
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(350b) Some of the Variables, Some of the Times, with Some Things Known: Identification with Partial Information
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(582a) Crystal Engineering of a Zeolite Using Machine Learning
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(298c) Quantifying the Invertibility of Neural Networks and Their Transformations
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(688g) Tipping Point Dynamics for Epidemiological Networks. Constructing Reduced Dynamical Data-Driven Models for Evolving Graphs
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(361n) Limits of Entrainment of Circadian Neuronal Networks
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(298d) Staying the Course: Locating Fixed Points of Dynamical Systems (and Critical Points of Potentials) on Riemannian Manifolds Defined By Sampling Point-Clouds
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(542c) A Data-Driven Approach to Determining Problem Well-Posedness
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(106c) Inverse Backward Analysis of Neural Approximants of Ordinary Differential Equations
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(624e) Accelerating Multiscale Global Optimization through Reduced Bayesian Optimization
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(542b) A Machine Learning Approach to Bridge the Gap between the Kuramoto-Sivashinsky and the Navier-Stokes Equations for Thin Film Flow
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(542d) Nonlinear Data Fusion from Heterogeneous Partial Observation Sets
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(542e) Learning What to Learn: Common and Sensor-Specific Information across Multiple Sensors, with Some Thoughts about Sensor Spoofing and Causality
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(182d) Modeling Tipping Points in the Reduced-Order Stochastic Dynamics of a Population of Interacting Agents
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(298e) Manifold Learning Post-Processing Galerkin Algorithms for Dissipative PDEs on Their Approximate Inertial Manifolds
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
(663e) Active Learning Workflow for Discovery of Stable Ternary Alloys from Binary Alloy Data
2023 AIChE Annual Meeting (ISBN: 978-0-8169-1120-2)
(545c) Generative Model Assisted Sampling of Multiscale Dynamical Systems
2023 AIChE Annual Meeting (ISBN: 978-0-8169-1120-2)
(373c) Data-Driven Disentanglement of Latent variables and Effective parameters for Cellular Trajectories with Manifold Learning and Deep Learning Approaches
2023 AIChE Annual Meeting (ISBN: 978-0-8169-1120-2)
(241a) Locating Saddle Points of Dynamical Systems: Gentlest Ascent Dynamics & Gradient Extremals on Manifolds Defined By Adaptively Sampled Point Clouds
2023 AIChE Annual Meeting (ISBN: 978-0-8169-1120-2)
(241g) Data-Driven Bifurcation Diagrams for Neural (Integro-) Differential Equations
2023 AIChE Annual Meeting (ISBN: 978-0-8169-1120-2)
(521eo) Programmable Catalytic Ammonia Synthesis and Its Optimization
2023 AIChE Annual Meeting (ISBN: 978-0-8169-1120-2)
(632c) Determining Polymer Size from Raman Spectroscopy2024 AIChE Annual Meeting
(519g) Linking Bayesian Optimization with Deterministic Global Gaussian Process Optimization for Active Determination of Optimal Policies: From Catalysis to Cancer Treatment2024 AIChE Annual Meeting
(310k) Navigating the Unknown: Efficiently Locating the Transition State of the Diels-Alder Reaction through Adaptively Sampled Point Clouds2024 AIChE Annual Meeting
(676d) Accelerating Optimization By Exploiting the Existence of Low Dimensional Manifolds2024 AIChE Annual Meeting
Associated proceedings
2018 AIChE Annual Meeting
2019 AIChE Annual Meeting
2020 Virtual AIChE Annual Meeting
2021 Annual Meeting
2022 Annual Meeting
2023 AIChE Annual Meeting