Panos Stinis - RankMe

72 papers A* 1Journal 69Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning. CoRR Wenqian Chen, Yucheng Fu, Michael Penwarden, Pratanu Roy, Panos Stinis
2026	J	`jnl`	Enhancing classification accuracy through chaos. CoRR Panos Stinis
2026	J	`jnl`	Improving the accuracy of physics-informed neural networks via last-layer retraining. CoRR Saad Qadeer, Panos Stinis
2026	J	`jnl`	SCORE: Statistical Certification of Regions of Attraction via Extreme Value Theory. CoRR Pietro Zanotta, Panos Stinis, Ján Drgona
2026	J	`jnl`	SINDy-KANs: Sparse identification of non-linear dynamics through Kolmogorov-Arnold networks. CoRR Amanda A. Howard, Nicholas Zolman, Bruno Jacob, Steven L. Brunton, Panos Stinis
2026	J	`jnl`	What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications. J. Comput. Phys. Emily Williams, Amanda A. Howard, Saad Qadeer, Brek Meuris, Panos Stinis
2025	J	`jnl`	Bridging quantum and classical computing for partial differential equations through multifidelity machine learning. CoRR Bruno Jacob, Amanda A. Howard, Panos Stinis
2025	J	`jnl`	Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning. CoRR Amirhossein Mollaali, Christian B. Moya, Amanda A. Howard, Alexander Heinlein, Panos Stinis, Guang Lin
2025	J	`jnl`	Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets. CoRR Adrienne M. Propp, Mauro Perego, Eric C. Cyr, Anthony Gruber, Amanda A. Howard, Alexander Heinlein, Panos Stinis, Daniel M. Tartakovsky
2025	J	`jnl`	E-PINNs: Epistemic Physics-Informed Neural Networks. CoRR Ashish S. Nair, Bruno Jacob, Amanda A. Howard, Ján Drgona, Panos Stinis
2025	J	`jnl`	Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks. CoRR Zhi-Feng Wei, Wenqian Chen, Panos Stinis
2025	J	`jnl`	From LIF to QIF: Toward Differentiable Spiking Neurons for Scientific Machine Learning. CoRR Ruyin Wan, George Em Karniadakis, Panos Stinis
2025	J	`jnl`	Multifidelity Kolmogorov-Arnold networks. Mach. Learn. Sci. Technol. Amanda A. Howard, Bruno Jacob, Panos Stinis
2025	J	`jnl`	Physics-Informed DeepONet Coupled with FEM for Convective Transport in Porous Media with Sharp Gaussian Sources. CoRR Erdi Kara, Panos Stinis
2025	J	`jnl`	SPIKANs: separable physics-informed Kolmogorov-Arnold networks. Mach. Learn. Sci. Technol. Bruno Jacob, Amanda A. Howard, Panos Stinis
2025	J	`jnl`	Self-adaptive weighting and sampling for physics-informed neural networks. CoRR Wenqian Chen, Amanda A. Howard, Panos Stinis
2025	J	`jnl`	Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks. J. Comput. Phys. Wenqian Chen, Amanda A. Howard, Panos Stinis
2025	J	`jnl`	Simulating Three-dimensional Turbulence with Physics-informed Neural Networks. CoRR Sifan Wang, Shyam Sankaran, Panos Stinis, Paris Perdikaris
2025	J	`jnl`	SpikePool: Event-driven Spiking Transformer with Pooling Attention. CoRR Donghyun Lee, Alex Sima, Yuhang Li, Panos Stinis, Priyadarshini Panda
2025	J	`jnl`	Stabilizing PDE-ML Coupled System. CoRR Saad Qadeer, Panos Stinis, Hui Wan
2024	J	`jnl`	A multifidelity approach to continual learning for physical systems. Mach. Learn. Sci. Technol. Amanda A. Howard, Yucheng Fu, Panos Stinis
2024	J	`jnl`	Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations. J. Comput. Phys. Wenqian Chen, Panos Stinis
2024	J	`jnl`	Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems. CoRR Amanda A. Howard, Bruno Jacob, Sarah H. Murphy, Alexander Heinlein, Panos Stinis
2024	J	`jnl`	Multifidelity Kolmogorov-Arnold Networks. CoRR Amanda A. Howard, Bruno Jacob, Panos Stinis
2024	J	`jnl`	Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems. CoRR Alexander Heinlein, Amanda A. Howard, Damien Beecroft, Panos Stinis
2024	J	`jnl`	SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics. J. Comput. Phys. Panos Stinis, C. Daskalakis, Paul J. Atzberger
2024	J	`jnl`	SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks. CoRR Bruno Jacob, Amanda A. Howard, Panos Stinis
2024	J	`jnl`	Scientific machine learning for closure models in multiscale problems: a review. CoRR Benjamin Sanderse, Panos Stinis, Romit Maulik, Shady E. Ahmed
2024	J	`jnl`	Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks. CoRR Wenqian Chen, Amanda A. Howard, Panos Stinis
2024	J	`jnl`	What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications. CoRR Emily Williams, Amanda A. Howard, Brek Meuris, Panos Stinis
2023	J	`jnl`	A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling. CoRR Qizhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis
2023	J	`jnl`	A Multifidelity deep operator network approach to closure for multiscale systems. CoRR Shady E. Ahmed, Panos Stinis
2023	J	`jnl`	A hybrid deep neural operator/finite element method for ice-sheet modeling. J. Comput. Phys. Qizhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis
2023	J	`jnl`	A multifidelity approach to continual learning for physical systems. CoRR Amanda A. Howard, Yucheng Fu, Panos Stinis
2023	J	`jnl`	Efficient kernel surrogates for neural network-based regression. CoRR Saad Qadeer, Andrew Engel, Adam Tsou, Max Vargas, Panos Stinis, Tony Chiang
2023	J	`jnl`	Exploring Learned Representations of Neural Networks with Principal Component Analysis. CoRR Amit Harlev, Andrew Engel, Panos Stinis, Tony Chiang
2023	J	`jnl`	Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations. CoRR Wenqian Chen, Panos Stinis
2023	J	`jnl`	Multifidelity deep operator networks for data-driven and physics-informed problems. J. Comput. Phys. Amanda A. Howard, Mauro Perego, George Em Karniadakis, Panos Stinis
2023	J	`jnl`	Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model. CoRR Wenqian Chen, Yucheng Fu, Panos Stinis
2023	J	`jnl`	Physics-informed machine learning of the correlation functions in bulk fluids. CoRR Wenqian Chen, Peiyuan Gao, Panos Stinis
2023	J	`jnl`	SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics. CoRR Panos Stinis, Constantinos Daskalakis, Paul J. Atzberger
2023	J	`jnl`	Stacked networks improve physics-informed training: applications to neural networks and deep operator networks. CoRR Amanda A. Howard, Sarah H. Murphy, Shady E. Ahmed, Panos Stinis
2023	J	`jnl`	ViTO: Vision Transformer-Operator. CoRR Oded Ovadia, Adar Kahana, Panos Stinis, Eli Turkel, George Em Karniadakis
2022	J	`jnl`	Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery. CoRR Qizhi He, Yucheng Fu, Panos Stinis, Alexandre M. Tartakovsky
2022	J	`jnl`	Multifidelity Deep Operator Networks. CoRR Amanda A. Howard, Mauro Perego, George E. Karniadakis, Panos Stinis
2022	J	`jnl`	SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations. CoRR Qian Zhang, Adar Kahana, George Em Karniadakis, Panos Stinis
2022	—	`conf`	Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling. MSML Kookjin Lee, Nathaniel Trask, Panos Stinis
2021	J	`jnl`	A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure. Multiscale Model. Simul. Jacob Roth, David A. Barajas-Solano, Panos Stinis, Jonathan Weare, Mihai Anitescu
2021	A*	`conf`	Machine learning structure preserving brackets for forecasting irreversible processes. NeurIPS Kookjin Lee, Nathaniel Trask, Panos Stinis
2021	J	`jnl`	Machine learning structure preserving brackets for forecasting irreversible processes. CoRR Kookjin Lee, Nathaniel A. Trask, Panos Stinis
2021	J	`jnl`	Machine-learning custom-made basis functions for partial differential equations. CoRR Brek Meuris, Saad Qadeer, Panos Stinis
2021	J	`jnl`	Optimal renormalization of multi-scale systems. CoRR Jacob Price, Brek Meuris, Madelyn Shapiro, Panos Stinis
2021	J	`jnl`	Physics-constrained deep neural network method for estimating parameters in a redox flow battery. CoRR Qizhi He, Panos Stinis, Alexandre M. Tartakovsky
2021	J	`jnl`	Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling. CoRR Kookjin Lee, Nathaniel Trask, Panos Stinis
2021	J	`jnl`	Time-dependent stochastic basis adaptation for uncertainty quantification. CoRR Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
2020	—	`conf`	Enforcing Constraints for Time Series Prediction in Supervised, Unsupervised and Reinforcement Learning. AAAI Spring Symposium: MLPS Panos Stinis
2019	J	`jnl`	A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations. CoRR Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre M. Tartakovsky
2019	J	`jnl`	Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. J. Comput. Phys. Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung
2019	J	`jnl`	Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning. CoRR Panos Stinis
2019	J	`jnl`	Model reduction for a power grid model. CoRR Jing Li, Panos Stinis
2019	J	`jnl`	Renormalized Reduced Order Models with Memory for Long Time Prediction. Multiscale Model. Simul. Jacob Price, Panos Stinis
2018	J	`jnl`	Doing the impossible: Why neural networks can be trained at all. CoRR Nathan O. Hodas, Panos Stinis
2018	J	`jnl`	Dynamic Looping of a Free-Draining Polymer. SIAM J. Appl. Math. Felix X.-F. Ye, Panos Stinis, Hong Qian
2018	J	`jnl`	Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. CoRR Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung
2018	J	`jnl`	Stochastic Basis Adaptation and Spatial Domain Decomposition for Partial Differential Equations with Random Coefficients. SIAM/ASA J. Uncertain. Quantification Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
2017	J	`jnl`	Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients. J. Comput. Phys. Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
2017	J	`jnl`	Solving differential equations with unknown constitutive relations as recurrent neural networks. CoRR Tobias Hagge, Panos Stinis, Enoch Yeung, Alexandre M. Tartakovsky
2016	J	`jnl`	A unified framework for mesh refinement in random and physical space. J. Comput. Phys. Jing Li, Panos Stinis
2015	J	`jnl`	Mesh refinement for uncertainty quantification through model reduction. J. Comput. Phys. Jing Li, Panos Stinis
2012	J	`jnl`	Improved particle filters for multi-target tracking. J. Comput. Phys. Vasileios Maroulas, Panos Stinis
2012	J	`jnl`	Numerical Computation of Solutions of the Critical Nonlinear Schrödinger Equation after the Singularity. Multiscale Model. Simul. Panos Stinis
2012	J	`jnl`	Stochastic global optimization as a filtering problem. J. Comput. Phys. Panos Stinis

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.