Rescale, a digital engineering platform built for the AI era, has secured funding through the Genesis Mission, a U.S. Department of Energy (DOE) initiative designed to create the world's most advanced integrated science discovery platform. As part of the Agentic HPC Pipeline Initiative (AHPI), Rescale will collaborate with Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, and Oak Ridge National Laboratory to leverage agentic AI for accelerating industry adoption of cutting-edge simulation software developed by U.S. national laboratories.

Created through an executive order in November 2025, the Genesis Mission brings together the DOE's network of 17 national laboratories and approximately 40,000 scientists and engineers to speed advances in energy, scientific research, and national security. The initiative combines artificial intelligence, high-performance computing, quantum technologies, and advanced scientific instruments into a unified discovery platform. DOE leaders have likened its scope and long-term impact to landmark national efforts such as the Manhattan Project and the Apollo program, with the goal of doubling U.S. scientific research productivity over the next decade.

Earlier this year, the DOE introduced the first major funding opportunity under the Genesis Mission, committing $293 million to projects addressing national priorities in advanced manufacturing, biotechnology, nuclear energy, critical materials, and quantum information science. The competition attracted more than 8,000 applications, the largest response ever received for a single DOE funding program and nearly three times the department's previous record. This marks the first phase of what is expected to become a multi-year investment program.

By earning Phase I funding, AHPI—and Rescale as a consortium member—was selected from an exceptionally competitive applicant pool. The initiative focuses on overcoming a longstanding challenge in federally funded research: although U.S. national laboratories have developed world-class engineering simulation software over several decades, widespread industrial adoption has remained limited because operating these sophisticated tools requires specialized expertise and computing infrastructure.

Rescale is the only commercial technology company participating in the AHPI consortium. Working alongside the three national laboratories, the company will integrate agentic AI capabilities into the WarpX, LiDO, and Adamantine simulation codes, originally developed by Lawrence Berkeley, Lawrence Livermore, and Oak Ridge National Laboratories, respectively. Through the Rescale platform, AI agents will assist engineers with selecting the appropriate simulation code, configuring input parameters, monitoring simulation progress, and interpreting results. The initiative aims to reduce the expertise and time needed to perform these simulations by more than five times, enabling U.S. manufacturers to independently access tools that previously required direct laboratory support.

"Lawrence Berkeley National Laboratory has spent decades advancing computational science, and an important part of our mission is ensuring those innovations benefit organizations beyond the laboratory," said Peter Nugent, Division Deputy for Science in the Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory and principal investigator for AHPI. "By working together with Lawrence Livermore and Oak Ridge through AHPI, we can make these powerful technologies more accessible to American manufacturers."

Joris Poort, CEO of Rescale, said the Genesis Mission underscores the importance of putting advanced AI capabilities into the hands of leading research and development teams to maximize the value of both public and private investment. He noted that Rescale's experience working with government agencies and industry over the past 15 years positions the company to help transition advanced AI-driven simulation technologies from national laboratories into practical use by U.S. manufacturers.

The award also strengthens Rescale's ongoing collaboration with Oak Ridge National Laboratory's Manufacturing Demonstration Facility, which already provides manufacturers with access to simulation tools developed at ORNL. AHPI seeks to address a common limitation in laboratory-industry partnerships: once a DOE-funded collaboration concludes, companies retain the knowledge gained during the project but often lose access to the specialized software and workflows that enabled those discoveries. By preserving AI-powered workflows, surrogate models, and configured simulation environments on the Rescale platform, AHPI will allow researchers and industry partners to continue collaborating long after individual DOE-funded projects have ended.

Rescale's inclusion in AHPI reflects growing recognition that making federally developed scientific software more accessible to commercial industry is essential for accelerating innovation and strengthening U.S. technological competitiveness.

Telecom providers are pushing for fast 6G standardization and quick adoption across enterprise and consumer markets, with AI playing a central role.

The transition to 6G marks a major shift — potentially becoming the first generation of wireless networks built to be AI-native. Because AI will deeply influence how 6G operates, engineers face a new challenge: validating systems that are far more adaptive, intelligent, and fast than previous generations.

This overview outlines how AI can support 6G design validation for teams in communication service providers, mobile operators, technology vendors, and device manufacturers. It also explores emerging applications enabled by AI and 6G, the types of AI techniques involved, and how these tools can streamline design and testing workflows.
 
What new opportunities will 6G and AI unlock?
AI and 6G together are expected to drive major innovations, including real-time digital twins, advanced manufacturing systems, highly autonomous transport, holographic communication, and widespread edge intelligence. These capabilities align with the visions of the ITU and 3GPP for 2030 and beyond.

Real-time digital twins
With widespread coverage, extremely low latency, and high throughput, 6G paired with AI could create high-fidelity, real-time digital counterparts of physical assets and environments. These digital twins would support modeling, control, analysis, and simulation with unprecedented accuracy. Digital twin networks could mirror actual network conditions to enable continuous optimization, especially when combined with integrated sensing and communication (ISAC).

Smart factories
AI-enhanced 6G connectivity could enable industrial automation at scale through reliable, ultra-responsive data exchange across robotics, industrial IoT, and intelligent devices. “Industrial 6G” may enable fully automated operations in environments such as factories, ports, and airports, supported by private 6G deployments.

Autonomous mobility
Next-generation mobility systems — from autonomous vehicles to intelligent transportation — will rely on AI-powered 6G capabilities. This includes AI-assisted driving, real-time mapping, and precise positioning for cellular vehicle-to-everything (C-V2X) interactions.

Holographic communication
Future 6G and AI infrastructure may support immersive communications such as holographic telepresence and multi-sensory remote interaction. AI-driven semantic communication could reduce bandwidth demands by transmitting only the essential meaning behind data-heavy content.

Distributed edge intelligence
6G is expected to blur the line between communication and computing by pushing AI models to the network edge. This could enable coordinated inference, collaborative robotics, and pervasive, real-time intelligence across devices.
 
How will AI improve 6G network design and operations?
6G will involve both physical elements (e.g., radios, base stations, user devices) and logical components (e.g., RAN, core network functions, protocol stacks). Many of these will be optimized using AI during design, validation, and even runtime.

AI-native air interface
AI could enhance key radio functions such as channel estimation, symbol detection, beam selection, modulation, and antenna configuration. These models may operate on devices, at the base station, or jointly across both.

AI-assisted beamforming
AI methods may support:

AI-optimized RAN
AI could enable a self-organizing RAN capable of real-time adaptation, end-to-end optimization, and autonomous performance tuning.

Automated network management
AI-driven operations may include predictive maintenance, traffic forecasting, energy optimization, and intelligent resource allocation. Real-time threat detection and mitigation could also be enhanced through AI analytics.
 
Which AI techniques are most useful for validating 6G performance?
A range of AI methods — deep learning, reinforcement learning, generative models, and more — will support system-level design and testing.

Reinforcement learning (RL)
RL is well-suited for automating decision-making in unpredictable environments and may be applied to:

Deep neural networks (DNNs)
DNNs may support tasks such as:

Transformer models
Transformer autoencoders may enhance CSI compression and feedback efficiency.

Graph neural networks (GNNs)
GNNs can model network topology and spatial relationships for interference control, mobility forecasting, and resource allocation.

Generative adversarial networks (GANs)
GANs can generate realistic channel data, support denoising, and detect anomalies.

Large reasoning/action models
These emerging agentic models may coordinate complex workflows and help test sophisticated, multi-component 6G systems.
 
How will synthetic AI data support 6G testing and validation?
AI-generated data will be crucial for exploring the huge range of possible 6G conditions — many of which cannot be physically tested early on.

Key synthetic-data methods include:

 
Can AI help validate 6G hardware and chip designs?
Yes. AI-powered anomaly detection, automation, and data-driven modeling could support the design of components for sub-THz frequencies, UM-MIMO, and other 6G features.
Key methods include:

 
What challenges come with using AI for 6G validation?
AI’s reliability isn’t guaranteed. Issues include out-of-distribution errors, limited data, low interpretability, overfitting, and hallucinations. To improve trustworthiness:

 
Keysight’s role
This summary illustrates how AI can support 6G design and testing. Keysight provides tools, research expertise, and 6G-ready test solutions to help engineering teams innovate with confidence throughout development.

Click here to know more.