4 papers
Deep vs. Shallow: Benchmarking Physics-Informed Neural Architectures on the Biharmonic Equation
Akshay Govind Srinivasan, Vikas Dwivedi, Balaji Srinivasan
Partial differential equation (PDE) solvers are fundamental to engineering simulation. Classical mesh-based approaches (finite difference/volume/element) are fast and accurate on h…
Towards Fast Option Pricing PDE Solvers Powered by PIELM
Akshay Govind Srinivasan, Anuj Jagannath Said, Sathwik Pentela +2
Partial differential equation (PDE) solvers underpin modern quantitative finance, governing option pricing and risk evaluation. Physics-Informed Neural Networks (PINNs) have emerge…
Enhancing Financial RAG with Agentic AI and Multi-HyDE: A Novel Approach to Knowledge Retrieval and Hallucination Reduction
Akshay Govind Srinivasan, Ryan Jacob George, Jayden Koshy Joe +6
Accurate and reliable knowledge retrieval is vital for financial question-answering, where continually updated data sources and complex, high-stakes contexts demand precision. Trad…
RE-GAINS & EnChAnT: Intelligent Tool Manipulation Systems For Enhanced Query Responses
Sahil Girhepuje, Siva Sankar Sajeev, Purvam Jain +7
Large Language Models (LLMs) currently struggle with tool invocation and chaining, as they often hallucinate or miss essential steps in a sequence. We propose RE-GAINS and EnChAnT,…