This paper presents a method for estimating and reconstructing the sound field within a room using physics-informed neural networks (PINNs). By incorporating a limited set of experimental room impulse responses (RIRs) as training data, the approach combines neural network processing capabilities with the underlying physics of sound propagation, as described by the wave equation. The network's ability to estimate particle velocity and intensity, in addition to sound pressure, demonstrates its capacity to represent the flow of acoustic energy and completely characterize the sound field with only a few measurements. Additionally, an investigation into the potential of this network as a tool for improving acoustic simulations is conducted. This is due to its proficiency in offering grid-free sound field mappings with minimal inference time. Furthermore, a study is carried out which encompasses comparative analyses against current approaches for sound field reconstruction. Specifically, the proposed approach is evaluated against both data-driven techniques and elementary wave-based regression methods. The results demonstrate that the physics-informed neural network stands out when reconstructing the early part of the room impulse response, while simultaneously allowing for complete sound field characterization in the time domain. The paper explores two contexts for employing PINNs: experimental measurements in a room and numerical simulations of the sound field in the same room. Using PINNs on the simulated sound field allows us to evaluate their potential as enhancements to simulations, enabling gridless representation and real-time auralization from any desired position within the enclosure. This opens up many possibilities for immersive auditory experiences. On the other hand, the experimental dataset showcases the PINN's adaptability to realistic scenarios, including limited measurements, measurement noise and other complexities. Despite these challenges, the PINN method enables a comprehensive characterization of the experimental sound field quantities. By investigating PINNs on these two sound fields, we gain insights into their benefits for real-time auralization and their effectiveness in addressing challenging and noisy real-world acoustic environments. The paper also compares the proposed PINN approach with alternative methods such as data-driven neural networks and wave-based regression. The results show that the PINN outperforms these methods in reconstructing the early part of the room impulse response and provides accurate sound field characterization. The PINN's ability to model the physics of sound propagation makes it a promising tool for sound field reconstruction, offering improved accuracy and computational efficiency compared to traditional methods. The study highlights the applicability of PINNs as a reliable tool for sound field reconstruction tasks, offering versatility in both simulated and real-world acoustic settings. As further research and advancements are made in the field of deep learning, PINNs emerge as a promising avenue for advancing sound field reconstruction, analysis, and reproduction methods. These networks promise to enhance the creation of immersive auditory experiences, aligning with the growing interest in this transformative domain.This paper presents a method for estimating and reconstructing the sound field within a room using physics-informed neural networks (PINNs). By incorporating a limited set of experimental room impulse responses (RIRs) as training data, the approach combines neural network processing capabilities with the underlying physics of sound propagation, as described by the wave equation. The network's ability to estimate particle velocity and intensity, in addition to sound pressure, demonstrates its capacity to represent the flow of acoustic energy and completely characterize the sound field with only a few measurements. Additionally, an investigation into the potential of this network as a tool for improving acoustic simulations is conducted. This is due to its proficiency in offering grid-free sound field mappings with minimal inference time. Furthermore, a study is carried out which encompasses comparative analyses against current approaches for sound field reconstruction. Specifically, the proposed approach is evaluated against both data-driven techniques and elementary wave-based regression methods. The results demonstrate that the physics-informed neural network stands out when reconstructing the early part of the room impulse response, while simultaneously allowing for complete sound field characterization in the time domain. The paper explores two contexts for employing PINNs: experimental measurements in a room and numerical simulations of the sound field in the same room. Using PINNs on the simulated sound field allows us to evaluate their potential as enhancements to simulations, enabling gridless representation and real-time auralization from any desired position within the enclosure. This opens up many possibilities for immersive auditory experiences. On the other hand, the experimental dataset showcases the PINN's adaptability to realistic scenarios, including limited measurements, measurement noise and other complexities. Despite these challenges, the PINN method enables a comprehensive characterization of the experimental sound field quantities. By investigating PINNs on these two sound fields, we gain insights into their benefits for real-time auralization and their effectiveness in addressing challenging and noisy real-world acoustic environments. The paper also compares the proposed PINN approach with alternative methods such as data-driven neural networks and wave-based regression. The results show that the PINN outperforms these methods in reconstructing the early part of the room impulse response and provides accurate sound field characterization. The PINN's ability to model the physics of sound propagation makes it a promising tool for sound field reconstruction, offering improved accuracy and computational efficiency compared to traditional methods. The study highlights the applicability of PINNs as a reliable tool for sound field reconstruction tasks, offering versatility in both simulated and real-world acoustic settings. As further research and advancements are made in the field of deep learning, PINNs emerge as a promising avenue for advancing sound field reconstruction, analysis, and reproduction methods. These networks promise to enhance the creation of immersive auditory experiences, aligning with the growing interest in this transformative domain.