| From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling WP Tsai, D Feng, M Pan, H Beck, K Lawson, Y Yang, J Liu, C Shen Nature communications 12 (1), 5988, 2021 | 134* | 2021 |
| Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data F Rahmani, K Lawson, W Ouyang, A Appling, S Oliver, C Shen Environmental Research Letters 16 (2), 024025, 2021 | 93 | 2021 |
| Transferring hydrologic data across continents–leveraging data‐rich regions to improve hydrologic prediction in data‐sparse regions K Ma, D Feng, K Lawson, WP Tsai, C Liang, X Huang, A Sharma, C Shen Water Resources Research 57 (5), e2020WR028600, 2021 | 85 | 2021 |
| Evaluating the potential and challenges of an uncertainty quantification method for long short‐term memory models for soil moisture predictions K Fang, D Kifer, K Lawson, C Shen Water Resources Research 56 (12), e2020WR028095, 2020 | 77 | 2020 |
| Differentiable modelling to unify machine learning and physical models for geosciences C Shen, AP Appling, P Gentine, T Bandai, H Gupta, A Tartakovsky, ... Nature Reviews Earth & Environment 4 (8), 552-567, 2023 | 73 | 2023 |
| Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy D Feng, J Liu, K Lawson, C Shen Water Resources Research 58 (10), e2022WR032404, 2022 | 71 | 2022 |
| Mitigating prediction error of deep learning streamflow models in large data‐sparse regions with ensemble modeling and soft data D Feng, K Lawson, C Shen Geophysical Research Letters 48 (14), e2021GL092999, 2021 | 61 | 2021 |
| The data synergy effects of time‐series deep learning models in hydrology K Fang, D Kifer, K Lawson, D Feng, C Shen Water Resources Research 58 (4), e2021WR029583, 2022 | 59 | 2022 |
| Impact of cathodic electron acceptor on microbial fuel cell internal resistance K Lawson, R Rossi, JM Regan, BE Logan Bioresource technology 316, 123919, 2020 | 58 | 2020 |
| Applications of deep learning in hydrology C Shen, K Lawson Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote …, 2021 | 45 | 2021 |
| Deep learning approaches for improving prediction of daily stream temperature in data‐scarce, unmonitored, and dammed basins F Rahmani, C Shen, S Oliver, K Lawson, A Appling Hydrological Processes 35 (11), e14400, 2021 | 44 | 2021 |
| Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy W Ouyang, K Lawson, D Feng, L Ye, C Zhang, C Shen Journal of Hydrology 599, 126455, 2021 | 44 | 2021 |
| A multiscale deep learning model for soil moisture integrating satellite and in situ data J Liu, F Rahmani, K Lawson, C Shen Geophysical Research Letters 49 (7), e2021GL096847, 2022 | 40 | 2022 |
| Development and evaluation of a trickle bed bioreactor for enhanced mass transfer and methanol production from biogas JP Sheets, K Lawson, X Ge, L Wang, Z Yu, Y Li Biochemical engineering journal 122, 103-114, 2017 | 39 | 2017 |
| The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment D Feng, H Beck, K Lawson, C Shen Hydrology and Earth System Sciences 27 (12), 2357-2373, 2023 | 24 | 2023 |
| The suitability of differentiable, learnable hydrologic models for ungauged regions and climate change impact assessment D Feng, H Beck, K Lawson, C Shen Hydrology and Earth System Sciences Discussions 2022, 1-28, 2022 | 19 | 2022 |
| Improving river routing using a differentiable Muskingum‐Cunge model and physics‐informed machine learning T Bindas, WP Tsai, J Liu, F Rahmani, D Feng, Y Bian, K Lawson, C Shen Water Resources Research 60 (1), e2023WR035337, 2024 | 11 | 2024 |
| Constructing a Large-scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates S Nagendra, SB Manjunatha, D Kifer, T Pei, W Li, K Lawson, H Nguyen, ... | 11 | 2021 |
| A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: Demonstration with photosynthesis simulations D Aboelyazeed, C Xu, FM Hoffman, J Liu, AW Jones, C Rackauckas, ... Biogeosciences 20 (13), 2671-2692, 2023 | 9 | 2023 |
| Revealing causal controls of storage-streamflow relationships with a data-centric bayesian framework combining machine learning and process-based modeling WP Tsai, K Fang, X Ji, K Lawson, C Shen Frontiers in Water 2, 583000, 2020 | 7 | 2020 |
Chaopeng ShenProfessor in Water Resources Engineering, Pennsylvania State University在 engr.psu.edu 的电子邮件经过验证
