Physics-Informed Neural Networks for Predicting Nitrous Oxide Flux

• URL: http://arxiv.org/abs/2607.23880v1
• Date: Sun, 26 Jul 2026 22:53:02 GMT
• Title: Physics-Informed Neural Networks for Predicting Nitrous Oxide Flux
• Abstract summary: Nitrous oxide (N$$O) is the dominant ozone-depleting substance emitted in the 21st century.<n>More than 70% of N$O emissions occur as a result of agricultural processes.<n>Current approaches to predicting N$O flux emissions include process-based models, as well as classical AI models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nitrous oxide (N$_2$O) is the dominant ozone-depleting substance emitted in the 21st century, and the third largest contributor to anthropogenic greenhouse gases due to its high potency and long atmospheric lifetime, with more than 70% of N$_2$O emissions occurring as a result of agricultural processes. Current approaches to predicting N$_2$O flux emissions include process-based models such as DayCent and Cycles, as well as classical AI models, but the application of Physics-Informed Neural Networks (PINNs) to predicting N$_2$O flux emissions is largely underexplored. Our paper draws upon the mechanistic equations that underlie the DayCent family of process-based models to construct a rigorously derived, literature-traceable physics residual. We then build and train an MLP-based PINN on a multi-site agricultural dataset spanning four geographically distinct US agricultural sites. Across all tested values of the physics loss weighting hyperparameter $λ$, our PINN consistently and substantially outperformed uncalibrated Cycles simulation (R$^2=0.01$), with our MLP baseline achieving mean R$^2=0.411$ across ten random seeds. Physics constraints consistently degrade model performance in holdout validation, with marginal degradation at low $λ$ and significant degradation at high $λ$, but consistently improve model performance and reduce performance variability in leave-one-site-out validation. This suggests that physics constraints sacrifice in-distribution accuracy for out-of-distribution robustness, anchoring the model toward biogeochemically plausible behavior on unfamiliar soil conditions --- though cross-site generalization remains challenging, with negative R$^2$ across all seeds and $λ$ values on our geographically distinct held-out site.

Related papers

• A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications [46.07145155913717]
  We propose a scalable operator-learning framework based on the Karhunen-Loeve Deep Neural Network (KL-DNN)<n>The model is trained on a dataset comprising 100 samples of large-scale simulations in a three-dimensional domain with 1.7 million cells and 50 time steps.
  arXiv  Detail & Related papers  (2026-06-26T18:15:24Z)
• How Neural Reward Models Learn Features for Policy Optimization: A Single-Index Analysis [53.063298916923976]
  We study the feedback in a Gaussian single-index model with $r*(x) = *(langle *, xrangle)$ and $x sim N(0, I_d)$.<n>We analyze a two-stage neural reward model that first learns the hidden direction $*$ from reward-weighted samples and then fits the readout layer by weighted ridge regression.
  arXiv  Detail & Related papers  (2026-05-23T22:00:38Z)
• Enhancing the interpretability of spatially variable N2O model predictions with soft sensors during wastewater treatment [0.0]
  We analysed how machine learning models predict disturbances to wastewater treatment plants.<n>Monitoring campaigns for nitrous oxide (N2O) emissions were simulated with a plant-wide mechanistic model.<n>We argue that N2O soft sensor model predictions are limited to the measuring location and the methodological uncertainty of the dataset.
  arXiv  Detail & Related papers  (2026-04-15T09:48:49Z)
• SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction [46.645872209565574]
  We present a specialized large-scale foundation model for virtual cell perturbation prediction.<n>First, we build a BioNeMo-based training and inference framework that substantially improves data throughput.<n>Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture.<n>Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics.
  arXiv  Detail & Related papers  (2026-03-18T05:51:08Z)
• Beyond the Hype: Comparing Lightweight and Deep Learning Models for Air Quality Forecasting [1.2744523252873352]
  This study investigates whether lightweight additive models -- Facebook Prophet (FBP) and NeuralProphet (NP) -- can deliver competitive forecasts for particulate matter in Beijing, China.<n>Using multi-year pollutant and meteorological data, we applied systematic feature selection (correlation, mutual information, mRMR), leakage-safe scaling, and chronological data splits.<n>Results show that FBP consistently outperformed NP, SARIMAX, and the learning-based baselines, achieving test $R2$ above 0.94 for both pollutants.
  arXiv  Detail & Related papers  (2025-12-09T19:39:45Z)
• Interactive Atmospheric Composition Emulation for Next-Generation Earth System Models [0.0]
  Interactive simulations in Earth System Models (ESMs) are computationally expensive as they transport numerous gaseous and aerosols at each timestep.<n>We extend the ModelE NINT tracer version using machine learning (ML) to create SmartINT, which emulates interactive emissions.<n>Smart NINT interactively calculates concentrations using ML with surface emissions meteorological data as inputs, avoiding full physics parameterizations.
  arXiv  Detail & Related papers  (2025-10-12T15:18:37Z)
• Fourier Neural Operator based surrogates for $CO_2$ storage in realistic geologies [57.23978190717341]
  We develop a Neural Operator (FNO) based model for real-time, high-resolution simulation of $CO$ plume migration.<n>The model is trained on a comprehensive dataset generated from realistic subsurface parameters.<n>We present various strategies for improving the reliability of predictions from the model, which is crucial while assessing actual geological sites.
  arXiv  Detail & Related papers  (2025-03-14T02:58:24Z)
• Atmospheric Transport Modeling of CO$_2$ with Neural Networks [46.26819563674888]
  Accurately describing the distribution of CO$$ in the atmosphere with atmospheric tracer transport models is essential for greenhouse gas monitoring and verification support systems. Large deep neural networks are poised to revolutionize weather prediction, which requires 3D modeling of the atmosphere. In this study we explore four different deep neural networks which have proven as state-of-the-art in weather prediction to assess their usefulness for atmospheric tracer transport modeling.
  arXiv  Detail & Related papers  (2024-08-20T17:33:20Z)
• Intelligent Agricultural Management Considering N$_2$O Emission and Climate Variability with Uncertainties [5.04035338843957]
  This study examines how artificial intelligence (AI) can be used in farming to boost crop yields, fine-tune use and watering, and reduce nitrate runoff and greenhouse gases. Facing climate change and limited agricultural knowledge, we use Partially Observable Markov Decision Processes (POMDPs) with a crop simulator to model AI agents' interactions with farming environments. Also, we develop Machine Learning (ML) models to predict N$$O emissions, integrating these predictions into the simulator.
  arXiv  Detail & Related papers  (2024-02-13T22:29:40Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano-particles in a contaminated aquifer [0.0]
  Engineered Nano-particles (ENPs) have emerged as an efficient reactive agent for the in-situ degradation of groundwater contaminants. The complex transport and retention mechanisms of ENPs hinder the development of an efficient remediation strategy. This work uses a dynamic, weight-enabled Physics-Informed Neural Network (dw-PINN) framework to model the nano-particle behavior within an aquifer.
  arXiv  Detail & Related papers  (2022-10-25T07:55:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.