1 citations · 1 across the 7 of their papers we have counts for
9 papers
Evaluation of optimisation and Bayesian inference methods for reaction rates in atmospheric chemical mechanisms
Valery Ashu, Wenqing Peng, Zhi-Song Liu +7
Constraining reaction rate coefficients is a central challenge in the development of explicit atmospheric chemical mechanisms, particularly for autoxidation systems where many reac…
TailWeather: from tail to extremes, a global climatological dataset for machine-learning weather forecasting
Zhi-Song Liu, Michael Boy, Risto Makkonen
Weather forecasting models commonly use the average forecast skill for short-range forecasts. However, it does not necessarily imply skill in the tails of the weather distribution.…
Inverse Neural Operator for ODE Parameter Optimization
Zhi-Song Liu, Wenqing Peng, Helmi Toropainen +5
We propose the Inverse Neural Operator (INO), a two-stage framework for recovering hidden ODE parameters from sparse, partial observations. In Stage 1, a Conditional Fourier Neural…
Cross-Resolution Attention Network for High-Resolution PM2.5 Prediction
Ammar Kheder, Helmi Toropainen, Wenqing Peng +3
Vision Transformers have achieved remarkable success in spatio-temporal prediction, but their scalability remains limited for ultra-high-resolution, continent-scale domains require…
TopoFlow: Topography-aware Pollutant Flow Learning for High-Resolution Air Quality Prediction
Ammar Kheder, Helmi Toropainen, Wenqing Peng +4
We propose TopoFlow (Topography-aware pollutant Flow learning), a physics-guided neural network for efficient, high-resolution air quality prediction. To explicitly embed physical…
SPIN-ODE: Stiff Physics-Informed Neural ODE for Chemical Reaction Rate Estimation
Wenqing Peng, Zhi-Song Liu, Michael Boy
Estimating rate coefficients from complex chemical reactions is essential for advancing detailed chemistry. However, the stiffness inherent in real-world atmospheric chemistry syst…
phepy: Visual benchmarks and improvements for out-of-distribution detectors
Felix Krumbiegel, Juniper Tyree, Michael Boy +2
Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on inputs outside its training domai…
Deep Spatio-Temporal Neural Network for Air Quality Reanalysis
Ammar Kheder, Benjamin Foreback, Lili Wang +2
Air quality prediction is key to mitigating health impacts and guiding decisions, yet existing models tend to focus on temporal trends while overlooking spatial generalization. We…
Neural Network Emulator for Atmospheric Chemical ODE
Zhi-Song Liu, Petri Clusius, Michael Boy
Modeling atmospheric chemistry is complex and computationally intense. Given the recent success of Deep neural networks in digital signal processing, we propose a Neural Network Em…