A JAX-based differentiable model of pressure vacuum swing adsorption accelerates cyclic steady-state simulation by 20x via Newton iteration and produces a better Pareto front with IPOPT than NSGA-II in two orders of magnitude less time on a post-combustion capture benchmark.
Miettinen,Nonlinear Multiobjective Optimization, vol
6 Pith papers cite this work. Polarity classification is still indexing.
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MatMind is a unified LLM-based generative model for crystals that reports lowest MAE on energy above hull, bulk modulus and band gap while achieving 65.3% S.U.N. rate on unconditional generation.
Compares weighted sums, achievement scalarizing functions, desirability functions, and a fuzzy-logic formulation on convex and concave analytical Pareto fronts for bi-criteria minimization, focusing on reachable regions and preference handling.
PPE is a novel predictor-corrector method for interactive Pareto set exploration in deep multi-task learning that approximates tangent spaces via Krylov subspace iterations using only matrix-vector products from automatic differentiation.
Proposes grading by Pareto surplus over a declared AI baseline to certify AI-resilient student performance in CS courses.
CaloArt achieves top FPD, high-level, and classifier metrics on CaloChallenge datasets 2 and 3 while keeping single-GPU generation at 9-11 ms per shower by combining large-patch tokenization, x-prediction, and conditional flow matching.
citing papers explorer
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Accelerating Simulation and Optimisation of Cyclic Adsorption Processes with Differentiable Programming
A JAX-based differentiable model of pressure vacuum swing adsorption accelerates cyclic steady-state simulation by 20x via Newton iteration and produces a better Pareto front with IPOPT than NSGA-II in two orders of magnitude less time on a post-combustion capture benchmark.
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MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science
MatMind is a unified LLM-based generative model for crystals that reports lowest MAE on energy above hull, bulk modulus and band gap while achieving 65.3% S.U.N. rate on unconditional generation.
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Comparing Scalar Objective Functions for Multi-Criteria Engineering Optimization
Compares weighted sums, achievement scalarizing functions, desirability functions, and a fuzzy-logic formulation on convex and concave analytical Pareto fronts for bi-criteria minimization, focusing on reachable regions and preference handling.
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Interactive Pareto navigation for deep multi-task learning
PPE is a novel predictor-corrector method for interactive Pareto set exploration in deep multi-task learning that approximates tangent spaces via Krylov subspace iterations using only matrix-vector products from automatic differentiation.
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Toward AI-Resilient Assessment in Computer Science Courses in an AI-Native World
Proposes grading by Pareto surplus over a declared AI baseline to certify AI-resilient student performance in CS courses.
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CaloArt: Large-Patch x-Prediction Diffusion Transformers for High-Granularity Calorimeter Shower Generation
CaloArt achieves top FPD, high-level, and classifier metrics on CaloChallenge datasets 2 and 3 while keeping single-GPU generation at 9-11 ms per shower by combining large-patch tokenization, x-prediction, and conditional flow matching.