AI Research

AI Unlocks Earth’s Subsurface Mysteries for Smart Energy Applications – USC Viterbi

Published

17 hours ago

July 9, 2025

The Hverir area in Iceland, known for its geothermal landscapes, is a key example of subsurface energy systems that AI research aims to improve, including geothermal energy and CO₂ storage. Photo/iStock.

Environmental scientists have amassed reams of data about the Earth’s surface and the vastness of its atmosphere.

As for the subterranean world?

Not nearly as much.

A new research project co-led by USC Viterbi’s Thomas Lord Department of Computer Science Professor Yan Liu aims to better understand and predict how water, carbon dioxide (CO₂), and energy move underground, which is critical for safe CO₂ storage, water management, and improving sustainable energy recovery.

PI Yan Liu and co-PI Behnam Jafarpour.

For instance, the results of the study could help scientists tackle such critical challenges as the safe underground storage of CO₂, a chemical compound that drives shifts in Earth’s energy balance.

CO₂ storage, also known as carbon capture and storage (CCS), is a process whereby carbon dioxide (CO₂) emissions from industrial sources or power plants are captured before they enter the atmosphere and then stored underground in geological formations, like depleted oil and gas fields or deep saline aquifers.

“CO₂ capture and storage is one of the grand challenges in geoscience, and our work has the potential to offer major breakthrough solutions to the accurate prediction of CO₂ storage,” said Liu, principal investigator of the study.

“Our work has the potential to offer major breakthrough solutions to the accurate prediction of CO₂ storage.” Yan Liu.

The research project also could aid in groundwater management and geothermal energy recovery, among other applications, added Liu, also a professor of electrical and computer engineering and biomedical sciences.

For example, she said, geoscientists would be able to better identify suitable storage reservoirs, predict their responses to development and operation strategies, and characterize important rock flow and transport properties.

Leveraging strengths

Liu is teaming up on the study with co-principal investigator Behnam Jafarpour, professor of chemical engineering and material science, electrical and computer engineering, and civil and environmental engineering.

The research project will employ a machine learning tool to solve some of the mysteries occurring below ground.

The three-year study, “Advancing Subsurface Flow and Transport Modeling with Physics-Informed Causal Deep Learning Models,” is supported by the U.S. National Science Foundation as part of its Collaborations in Artificial Intelligence and Geosciences (CAIG) program.

“Collaboration between geoscientists and computer scientists is essential.” Behnam Jafarpour

“Collaboration between geoscientists and computer scientists is essential for advancing subsurface flow and transport modeling by harnessing recent breakthroughs in AI and machine learning,” Jafarpour said. “The key lies in seamlessly integrating reliable domain knowledge and physical principles with AI algorithms to develop innovative technologies that leverage the strengths of both fields.”

A ‘paradigm shift’

Rocks, fractures, and fluids interact in a complex way below Earth’s surface, making it difficult to predict their behavior.

In particular, rock deposits form intricate structures and layers often exhibit complex fluid flow patterns in subsurface environments. Predicting the dynamics of the emerging flow patterns in complex geologic formations is paramount for managing the development of underlying resources.

By combining physical science and data that will be generated by an AI deep-learning model called PINCER (Physics-Informed Causal Deep Learning Models), Liu and Jafarpour hope to create a way to better capture and predict subsurface flow and transport dynamics.

The study launched in mid-September 2024 and is estimated to last through Aug. 31, 2027.

“PINCER presents a paradigm shift from traditional data-driven approaches or model-based techniques to a hybrid solution that combines the benefits of both methods,” an abstract of the study explains. “(It) advances geoscience research by developing more efficient and robust modeling and prediction of fluid flow and transport processes in subsurface environments.”

A clearer picture

As Liu explained, simulation systems have been used for decades to predict the subsurface flow dynamics, “but these models have their limitations,” she said. She explained that they rely heavily on highly uncertain inputs and are based on simplified descriptions of the underlying physics.

The new AI tool will build up the dataset from what is now a small amount of data, she said.

With a clearer picture of the underground dynamics, identifying suitable sites for underground CO₂ storage, for example, will become less of a guessing game, thus reducing the risk of accidental leaks due to unanticipated movements of subterranean materials.

Standard AI tools rely heavily on large training datasets and may produce predictions that deviate from the governing principles of subsurface flow systems, according to Jafarpour.

“The hope is that customized solutions like PINCER can help mitigate these limitations by enhancing physical consistency and reducing the data requirements of AI models,” he said.

AI techniques in geosciences

Two other USC studies were funded in the NSF grant package, one involving paleoclimatology and the other earthquake dynamics.

The NSF aims to advance the development and implementation of innovative AI techniques in geosciences to help better understand extreme weather, solar activity, earthquake hazards, and more.

The CAIG grants, announced in August 2024, require the collaboration of geoscientists, computer scientists, mathematicians, and others.

Liu and Jafarpour had received seed funding from the USC Ershaghi Center for Energy Transition to start their collaboration in this important area.

Published on July 9th, 2025

Last updated on July 9th, 2025

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AI Research

The Media and I: Artificial Intelligence

Published

18 minutes ago

July 10, 2025

The Editors

Lars and I discussed the role of AI in education and beyond, starting with a nostalgic nod to calculators and slide rules.

When I spoke with Lars recently, I emphasized the critical balance between using tools like AI and actually learning how to think, reason, and create. We touched on the incredible capabilities of AI, raising exciting possibilities and serious concerns. For example, AI-driven customer service can be frustratingly impersonal—I yelled at one for eight minutes earlier that day just trying to reach a human. In education, some elite universities are already requiring handwritten essays again, like with Blue Books, to preserve that integrity. However, as I have written previously about AI and medicine, it has the potential to increase accessibility and the quality of care.

You can find the whole conversation here.

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AI Research

China outpacing rest of the world in AI research – report

Published

58 minutes ago

July 10, 2025

The Editors

Shutterstock.com/Billion Photos

China is outpacing the rest of the world in artificial intelligence research at a time when AI is becoming a “strategic asset” akin to energy or military capability, according to a report from research technology company Digital Science.

The report – entitled DeepSeek and the New Geopolitics of AI: China’s ascent to research pre-eminence in AI – has been authored by Digital Science CEO Daniel Hook based on data from Dimensions, the world’s largest and most comprehensive database describing the global research ecosystem.

The news comes just a day after a report from Clarivate revealed that China is also leading the way in research output across G20 nations.

Dr Hook has analysed AI research data from the year 2000 to 2024, tracking trends in research collaborations and placing these within geopolitical, economic, and technological contexts. His report says AI research has grown at an “impressive rate” globally since the turn of the millennium – from just under 10,000 publications in 2000, to 60,000 publications in 2024.

Dr Hook’s key findings include:

China has become the pre-eminent world power in AI research, leading not only by research volume, but also by citation attention, and influence, rapidly increasing its lead on the rest of the world over the past seven years.
The US continues to have the strongest AI start-up scene, but China is catching up fast.
In 2024, China’s AI research publication output matched the combined output of the US, UK, and European Union (EU-27), and now commands more than 40% of global citation attention.
Despite global tensions, China has become the top collaborator for the US, UK, and EU in AI research, while needing less reciprocal collaboration than any of them.
China’s AI talent pool dwarfs its rivals – with 30,000 active AI researchers and a massive student and postdoctoral population.
The EU benefits from strong internal AI collaboration across its research bloc.
China dominates AI-related patents – patent filings and company-affiliated AI research show China outpacing the US tenfold in some indicators, underscoring its capacity to translate research into innovation.

“AI is no longer neutral – governments are using it as a strategic asset, akin to energy or military capability, and China is actively leveraging this advantage,” Hook says. “Governments need to understand the local, national and geostrategic implications of AI, with the underlying concern that lack of AI capability or capacity could be damaging from economic, political, social, and military perspectives.”

Hook says China is “massively and impressively” growing its AI research capacity. Unlike Western nations with clustered AI hubs, he says China boasts 156 institutions publishing more than 50 AI papers each in 2024, supporting a nationwide innovation ecosystem. In addition, “China’s AI workforce is young, growing fast, and uniquely positioned for long-term innovation.”

He says one sign of China’s rapidly developing capabilities is its release of the DeepSeek chatbot in January this year. “The emergence of DeepSeek is not merely a technological innovation – it is a symbol of a profound shift in the global AI landscape. DeepSeek exemplifies China’s technological independence. Its cost-efficient, open-source LLM demonstrates the country’s ability to innovate around US chip restrictions and dominate AI development at scale.”

The report comments further on the AI research landscape in the US, UK and EU. It says the UK remains “small but globally impactful”. “Despite its modest size, the UK consistently punches above its weight in attention-per-output metrics.”

However, the EU “risks falling behind in translation and visibility”. “The EU shows weaker international collaboration beyond its borders and struggles to convert research into applied outputs (e.g., patents), raising concerns about its future AI competitiveness.”

Discover more in the full report: DeepSeek and the New Geopolitics of AI: China’s ascent to research pre-eminence in AI.

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China produces more AI research than US, UK and EU combined

Published

58 minutes ago

July 10, 2025

Tash Mosheim

China led the world in artificial intelligence research in 2024, producing the largest volume of publications after developing a “nationwide innovation ecosystem”, according to a new report.

Despite rising geopolitical tensions, China was also the top AI research collaborator for the US, UK and European Union.

The study, DeepSeek and the New Geopolitics of AI: China’s ascent to research pre-eminence in AI, is published by research technology company Digital Science and authored by its chief executive, Daniel Hook.

The analysis used global data from its Dimensions database, covering research publication and collaboration trends from 2000 to 2024.

It says that AI research has grown at an “impressive rate” globally, expanding from just under 10,000 publications in 2000 to 60,000 in 2024.

China was found to be “the pre-eminent world power in AI research,” leading not only by research volume but also by “citation attention, and influence,” with its lead over the rest of the world growing rapidly over the past seven years.

In 2024, China’s publication output in AI research matched the combined output of the US, UK and EU, while its share of global citation attention exceeded 40 per cent.

Despite political tensions, the report finds that China has become the strongest AI research collaborator for the US, UK and EU, while itself needing “less reciprocal collaboration than any of them”.

China is also said to have the largest AI talent pool, with 30,000 active researchers and a large student and postdoctoral population, supporting what the report calls a “nationwide innovation ecosystem”.

The report highlights that 156 Chinese institutions each published more than 50 AI papers in 2024, contrasting with the more clustered research hubs in the West.

Alongside academic research, China also dominated AI-related patents. Patent filings and company-affiliated AI research showed China outpacing the US tenfold in some indicators, according to the report, reflecting the country’s ability to translate research into innovation.

Hook argued that AI had become a “strategic asset, akin to energy or military capability”, adding that “China is actively leveraging this advantage”.

He added: “Governments need to understand the local, national and geostrategic implications of AI, with the underlying concern that lack of AI capability or capacity could be damaging from economic, political, social and military perspectives.”

The release of the DeepSeek chatbot in January 2025 was cited as one example of China’s emerging capabilities.

“The emergence of DeepSeek is not merely a technological innovation – it is a symbol of a profound shift in the global AI landscape,” Hook said.

He described DeepSeek as demonstrating “China’s technological independence”, calling it a “cost-efficient, open-source LLM” that showed the country’s ability to “innovate around US chip restrictions and dominate AI development at scale”.

The report also describes the UK as “small but globally impactful”, saying that despite its modest size, the country consistently punches above its weight in “attention-per-output metrics”.

The EU, by contrast, “risks falling behind in translation and visibility.”

The report says that while the EU benefits from strong internal collaboration, it “shows weaker international collaboration beyond its borders and struggles to convert research into applied outputs (patents, for example), raising concerns about its future AI competitiveness”.

tash.mosheim@timeshighereducation.com

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